-----------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\Wilfred\Dropbox\Chow&HanPapers\Race&Refugees\Replication Files\CH_jop_appendix.
> log
  log type:  text
 opened on:   8 Jul 2022, 11:21:06

. 
. set scheme plottig // set graphical scheme to plottig

. 
. ********************************************************************************
. *********************** Appendix A2 ********************************************
. ********************************************************************************
. 
. ***** Figure A2
. use "CH_pretest.dta", clear

. gen race = 1 if group=="white-male"
(1,302 missing values generated)

.         replace race = 2 if group=="black-male"
(186 real changes made)

.         replace race = 3 if group=="hispanic-male"
(186 real changes made)

.         replace race = 4 if group=="asian-male"
(186 real changes made)

.         replace race = 5 if group=="white-female"
(186 real changes made)

.         replace race = 6 if group=="black-female"
(186 real changes made)

.         replace race = 7 if group=="hispanic-female"
(186 real changes made)

.         replace race = 8 if group=="asian-female"
(186 real changes made)

.         
. 
. *** gen mean and sd for age
. egen ageM = mean(age), by(race)

. format ageM %9.1f

. egen ageSD = sd(age), by(race)

. gen age_lower = ageM-ageSD

. gen age_upper = ageM+ageSD

. 
. twoway (rcap age_lower age_upper race, ///
>                 xlabel(1 `""White" "Men""' 2 `""Black" "Men""' ///
>                 3 `""Brown" "Men""'     4 `""Asian" "Men""' ///
>                 5 `""White" "Women""' 6 `""Black" "Women""' ///
>                 7 `""Brown" "Women""' 8 `""Asian" "Women""') ///
>                 xtitle("Race and Gender of Picture") ///
>                 ytitle("Mean Age (yrs)")) ///
>            (scatter ageM race, color(black) ///
>             mlabel(ageM) mlabcolor(black) mlabposition(1)), ///
>             legend(off) title("(a) Age" ) 

. graph save "age.gph", replace                   
(note: file age.gph not found)
(file age.gph saved)

. 
. *** gen mean and sd for attract
. egen attractM = mean(attract), by(race)

. format attractM %9.1f

. egen attractSD = sd(attract), by(race)

. gen attract_lower = attractM-attractSD

. gen attract_upper = attractM+attractSD

. 
. twoway (rcap attract_lower attract_upper race, ///
>                 xlabel(1 `""White" "Men""' 2 `""Black" "Men""' ///
>                 3 `""Brown" "Men""'     4 `""Asian" "Men""' ///
>                 5 `""White" "Women""' 6 `""Black" "Women""' ///
>                 7 `""Brown" "Women""' 8 `""Asian" "Women""') ///
>                 xtitle("Race and Gender of Picture") ///
>                 ytitle("Mean attract (yrs)")) ///
>            (scatter attractM race, color(black) ///
>             mlabel(attractM) mlabcolor(black) mlabposition(1)), ///
>             legend(off) title("(b) Attractiveness" ) 

. graph save "attract.gph", replace                       
(note: file attract.gph not found)
(file attract.gph saved)

. 
. *** gen mean and sd for competence
. egen compM = mean(comp), by(race)

. format compM %9.1f

. egen compSD = sd(comp), by(race)

. gen comp_lower = compM-compSD

. gen comp_upper = compM+compSD

. 
. twoway (rcap comp_lower comp_upper race, ///
>                 xlabel(1 `""White" "Men""' 2 `""Black" "Men""' ///
>                 3 `""Brown" "Men""'     4 `""Asian" "Men""' ///
>                 5 `""White" "Women""' 6 `""Black" "Women""' ///
>                 7 `""Brown" "Women""' 8 `""Asian" "Women""') ///
>                 xtitle("Race and Gender of Picture") ///
>                 ytitle("Mean comp (yrs)")) ///
>            (scatter compM race, color(black) ///
>             mlabel(compM) mlabcolor(black) mlabposition(1)), ///
>             legend(off) title("(c) Competence" ) 

. graph save "comp.gph", replace                  
(note: file comp.gph not found)
(file comp.gph saved)

.                 
. *** gen mean and sd for likeability
. egen likeM = mean(like), by(race)

. format likeM %9.1f

. egen likeSD = sd(like), by(race)

. gen like_lower = likeM-likeSD

. gen like_upper = likeM+likeSD

. 
. twoway (rcap like_lower like_upper race, ///
>                 xlabel(1 `""White" "Men""' 2 `""Black" "Men""' ///
>                 3 `""Brown" "Men""'     4 `""Asian" "Men""' ///
>                 5 `""White" "Women""' 6 `""Black" "Women""' ///
>                 7 `""Brown" "Women""' 8 `""Asian" "Women""') ///
>                 xtitle("Race and Gender of Picture") ///
>                 ytitle("Mean like (yrs)")) ///
>            (scatter likeM race, color(black) ///
>             mlabel(likeM) mlabcolor(black) mlabposition(1)), ///
>             legend(off) title("(d) Likeability" ) 

. graph save "like.gph", replace          
(note: file like.gph not found)
(file like.gph saved)

. 
. // combine saved graphs into a single figure                                                    
. graph combine "age.gph" "attract.gph" "comp.gph" "like.gph", xcommon ycommon    
(note:  clockdir by_legend_position not found in scheme, default attributes used)
(note:  clockdir by_legend_position not found in scheme, default attributes used)

.                 
. ********************************************************************************
. *********************** Appendix A3 ********************************************
. ********************************************************************************
. 
. *** Table A1 Sample Statitics Characteristics
. // Main Data
. use "CH_main.dta", clear

. 
. tabout female race age party edu using "tableA1.csv", ptotal(none) ///
>            oneway cells(col) clab(2019) style(csv) format(2) replace

Table output written to: tableA1.csv

,2019
female,
0,50.06
1,49.94

race,
1,68.77
2,10.80
3,13.09
4,7.35

age,
1,15.88
2,18.93
3,19.12
4,14.67
5,14.63
6,16.78

party,
1,32.97
2,38.08
3,28.95

edu,
1,30.21
2,31.89
3,23.40
4,14.50

.         
. // 2021 Data
. use "CH_followup.dta", clear

. 
. tabout female race age party edu using "tableA1.csv", ptotal(none) ///
>            oneway cells(col) clab(2021) style(csv) format(2) append

Table output written to: tableA1.csv

,2019
female,
0,50.06
1,49.94

race,
1,68.77
2,10.80
3,13.09
4,7.35

age,
1,15.88
2,18.93
3,19.12
4,14.67
5,14.63
6,16.78

party,
1,32.97
2,38.08
3,28.95

edu,
1,30.21
2,31.89
3,23.40
4,14.50

,2021
female,
0,48.66
1,51.34

race,
1,66.19
2,15.52
3,12.89
4,5.40

age,
1,13.83
2,19.44
3,16.55
4,16.28
5,16.00
6,17.89

party,
1,33.55
2,35.20
3,31.25

edu,
1,30.35
2,37.41
3,20.85
4,11.39

. 
. 
. *** Table A2 Sample Treatment Balance
. use "CH_main.dta", clear

. 
. // Tabout generates summary tables as .csv
. tabout female panel using "tableA2.csv" if outcome==1, ptotal(none) ///
>         cells(col) style(csv) format(2) h1(UNHCR Report Not Critical of U.S.) ///
>         h2(| White-Male | Mixed-Male | White-Mixed | Mixed-Mixed) replace

Table output written to: tableA2.csv

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
female,%,%,%,%,%
0,47.87,53.11,49.22,45.86,49.03
1,52.13,46.89,50.78,54.14,50.97

. tabout race panel using "tableA2.csv" if outcome==1, ptotal(none) ///
>         cells(col) style(csv) format(2)  h1(UNHCR Report Not Critical of U.S.) ///
>         h2(| White-Male | Mixed-Male | White-Mixed | Mixed-Mixed) append

Table output written to: tableA2.csv

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
female,%,%,%,%,%
0,47.87,53.11,49.22,45.86,49.03
1,52.13,46.89,50.78,54.14,50.97

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
race,%,%,%,%,%
1,70.72,68.15,65.29,69.81,68.50
2,9.97,8.60,11.15,8.12,9.47
3,9.97,16.56,13.69,14.29,13.60
4,9.35,6.69,9.87,7.79,8.43

. tabout age panel using "tableA2.csv" if outcome==1, ptotal(none) ///
>         cells(col) style(csv) format(2)  h1(UNHCR Report Not Critical of U.S.) ///
>         h2(| White-Male | Mixed-Male | White-Mixed | Mixed-Mixed) append

Table output written to: tableA2.csv

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
female,%,%,%,%,%
0,47.87,53.11,49.22,45.86,49.03
1,52.13,46.89,50.78,54.14,50.97

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
race,%,%,%,%,%
1,70.72,68.15,65.29,69.81,68.50
2,9.97,8.60,11.15,8.12,9.47
3,9.97,16.56,13.69,14.29,13.60
4,9.35,6.69,9.87,7.79,8.43

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
age,%,%,%,%,%
1,14.63,14.91,15.89,19.43,16.19
2,21.65,16.77,15.89,20.70,18.75
3,20.73,18.94,21.50,16.56,19.46
4,13.11,13.04,15.58,11.46,13.31
5,14.63,13.04,12.46,17.20,14.32
6,15.24,23.29,18.69,14.65,17.98

. tabout party panel using "tableA2.csv" if   outcome==1, ptotal(none) ///
>         cells(col) style(csv) format(2)  h1(UNHCR Report Not Critical of U.S.) ///
>         h2(| White-Male | Mixed-Male | White-Mixed | Mixed-Mixed) append

Table output written to: tableA2.csv

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
female,%,%,%,%,%
0,47.87,53.11,49.22,45.86,49.03
1,52.13,46.89,50.78,54.14,50.97

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
race,%,%,%,%,%
1,70.72,68.15,65.29,69.81,68.50
2,9.97,8.60,11.15,8.12,9.47
3,9.97,16.56,13.69,14.29,13.60
4,9.35,6.69,9.87,7.79,8.43

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
age,%,%,%,%,%
1,14.63,14.91,15.89,19.43,16.19
2,21.65,16.77,15.89,20.70,18.75
3,20.73,18.94,21.50,16.56,19.46
4,13.11,13.04,15.58,11.46,13.31
5,14.63,13.04,12.46,17.20,14.32
6,15.24,23.29,18.69,14.65,17.98

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
party,%,%,%,%,%
1,33.44,35.58,34.08,33.11,34.05
2,36.56,36.54,38.26,38.36,37.42
3,30.00,27.88,27.65,28.52,28.53

. tabout female panel using "tableA2.csv" if   outcome==2, ptotal(none) ///
>         cells(col) style(csv) format(2)  h1(UNHCR Report Critical of U.S.) ///
>         h2(| White-Male | Mixed-Male | White-Mixed | Mixed-Mixed) append

Table output written to: tableA2.csv

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
female,%,%,%,%,%
0,47.87,53.11,49.22,45.86,49.03
1,52.13,46.89,50.78,54.14,50.97

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
race,%,%,%,%,%
1,70.72,68.15,65.29,69.81,68.50
2,9.97,8.60,11.15,8.12,9.47
3,9.97,16.56,13.69,14.29,13.60
4,9.35,6.69,9.87,7.79,8.43

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
age,%,%,%,%,%
1,14.63,14.91,15.89,19.43,16.19
2,21.65,16.77,15.89,20.70,18.75
3,20.73,18.94,21.50,16.56,19.46
4,13.11,13.04,15.58,11.46,13.31
5,14.63,13.04,12.46,17.20,14.32
6,15.24,23.29,18.69,14.65,17.98

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
party,%,%,%,%,%
1,33.44,35.58,34.08,33.11,34.05
2,36.56,36.54,38.26,38.36,37.42
3,30.00,27.88,27.65,28.52,28.53

UNHCR Report Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
female,%,%,%,%,%
0,51.27,49.85,51.11,52.20,51.10
1,48.73,50.15,48.89,47.80,48.90

. tabout race panel using "tableA2.csv" if outcome==2, ptotal(none) ///
>         cells(col) style(csv) format(2)  h1(UNHCR Report Critical of U.S.) ///
>         h2(| White-Male | Mixed-Male | White-Mixed | Mixed-Mixed) append

Table output written to: tableA2.csv

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
female,%,%,%,%,%
0,47.87,53.11,49.22,45.86,49.03
1,52.13,46.89,50.78,54.14,50.97

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
race,%,%,%,%,%
1,70.72,68.15,65.29,69.81,68.50
2,9.97,8.60,11.15,8.12,9.47
3,9.97,16.56,13.69,14.29,13.60
4,9.35,6.69,9.87,7.79,8.43

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
age,%,%,%,%,%
1,14.63,14.91,15.89,19.43,16.19
2,21.65,16.77,15.89,20.70,18.75
3,20.73,18.94,21.50,16.56,19.46
4,13.11,13.04,15.58,11.46,13.31
5,14.63,13.04,12.46,17.20,14.32
6,15.24,23.29,18.69,14.65,17.98

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
party,%,%,%,%,%
1,33.44,35.58,34.08,33.11,34.05
2,36.56,36.54,38.26,38.36,37.42
3,30.00,27.88,27.65,28.52,28.53

UNHCR Report Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
female,%,%,%,%,%
0,51.27,49.85,51.11,52.20,51.10
1,48.73,50.15,48.89,47.80,48.90

UNHCR Report Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
race,%,%,%,%,%
1,66.56,69.43,71.43,68.71,69.04
2,12.25,13.38,11.69,11.29,12.16
3,14.90,10.83,11.36,13.23,12.56
4,6.29,6.37,5.52,6.77,6.24

. tabout age panel using "tableA2.csv" if outcome==2, ptotal(none) ///
>         cells(col) style(csv) format(2)  h1(UNHCR Report Critical of U.S.) ///
>         h2(| White-Male | Mixed-Male | White-Mixed | Mixed-Mixed) append

Table output written to: tableA2.csv

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
female,%,%,%,%,%
0,47.87,53.11,49.22,45.86,49.03
1,52.13,46.89,50.78,54.14,50.97

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
race,%,%,%,%,%
1,70.72,68.15,65.29,69.81,68.50
2,9.97,8.60,11.15,8.12,9.47
3,9.97,16.56,13.69,14.29,13.60
4,9.35,6.69,9.87,7.79,8.43

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
age,%,%,%,%,%
1,14.63,14.91,15.89,19.43,16.19
2,21.65,16.77,15.89,20.70,18.75
3,20.73,18.94,21.50,16.56,19.46
4,13.11,13.04,15.58,11.46,13.31
5,14.63,13.04,12.46,17.20,14.32
6,15.24,23.29,18.69,14.65,17.98

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
party,%,%,%,%,%
1,33.44,35.58,34.08,33.11,34.05
2,36.56,36.54,38.26,38.36,37.42
3,30.00,27.88,27.65,28.52,28.53

UNHCR Report Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
female,%,%,%,%,%
0,51.27,49.85,51.11,52.20,51.10
1,48.73,50.15,48.89,47.80,48.90

UNHCR Report Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
race,%,%,%,%,%
1,66.56,69.43,71.43,68.71,69.04
2,12.25,13.38,11.69,11.29,12.16
3,14.90,10.83,11.36,13.23,12.56
4,6.29,6.37,5.52,6.77,6.24

UNHCR Report Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
age,%,%,%,%,%
1,16.56,12.92,15.87,16.98,15.57
2,19.43,18.77,20.32,17.92,19.10
3,16.56,22.15,16.51,19.81,18.79
4,15.29,16.00,17.46,15.41,16.04
5,15.29,13.85,15.56,15.09,14.94
6,16.88,16.31,14.29,14.78,15.57

. tabout party panel using "tableA2.csv" if outcome==2, ptotal(none) ///
>         cells(col) style(csv) format(2)  h1(UNHCR Report Critical of U.S.) ///
>         h2(| White-Male | Mixed-Male | White-Mixed | Mixed-Mixed) append

Table output written to: tableA2.csv

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
female,%,%,%,%,%
0,47.87,53.11,49.22,45.86,49.03
1,52.13,46.89,50.78,54.14,50.97

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
race,%,%,%,%,%
1,70.72,68.15,65.29,69.81,68.50
2,9.97,8.60,11.15,8.12,9.47
3,9.97,16.56,13.69,14.29,13.60
4,9.35,6.69,9.87,7.79,8.43

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
age,%,%,%,%,%
1,14.63,14.91,15.89,19.43,16.19
2,21.65,16.77,15.89,20.70,18.75
3,20.73,18.94,21.50,16.56,19.46
4,13.11,13.04,15.58,11.46,13.31
5,14.63,13.04,12.46,17.20,14.32
6,15.24,23.29,18.69,14.65,17.98

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
party,%,%,%,%,%
1,33.44,35.58,34.08,33.11,34.05
2,36.56,36.54,38.26,38.36,37.42
3,30.00,27.88,27.65,28.52,28.53

UNHCR Report Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
female,%,%,%,%,%
0,51.27,49.85,51.11,52.20,51.10
1,48.73,50.15,48.89,47.80,48.90

UNHCR Report Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
race,%,%,%,%,%
1,66.56,69.43,71.43,68.71,69.04
2,12.25,13.38,11.69,11.29,12.16
3,14.90,10.83,11.36,13.23,12.56
4,6.29,6.37,5.52,6.77,6.24

UNHCR Report Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
age,%,%,%,%,%
1,16.56,12.92,15.87,16.98,15.57
2,19.43,18.77,20.32,17.92,19.10
3,16.56,22.15,16.51,19.81,18.79
4,15.29,16.00,17.46,15.41,16.04
5,15.29,13.85,15.56,15.09,14.94
6,16.88,16.31,14.29,14.78,15.57

UNHCR Report Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
party,%,%,%,%,%
1,30.64,31.73,33.33,31.70,31.86
2,41.41,39.74,34.98,38.89,38.75
3,27.95,28.53,31.68,29.41,29.39

. 
. *** Table A3 Sample Treatment Balance (w/o country labels)
. // Use followup data
. use "CH_followup.dta", clear

. 
. // Tabout generates summary tables as .csv
. tabout female panel using "tableA3.csv" if country==1 & outcome==1, ptotal(none) ///
>         cells(col) style(csv) format(2) h1(UNHCR Report Not Critical of U.S.) ///
>         h2(| White-Male | Mixed-Male | White-Mixed | Mixed-Mixed) replace

Table output written to: tableA3.csv

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
female,%,%,%,%,%
0,44.32,51.33,49.48,46.41,47.75
1,55.68,48.67,50.52,53.59,52.25

. tabout race panel using "tableA3.csv" if country==1 & outcome==1, ptotal(none) ///
>         cells(col) style(csv) format(2)  h1(UNHCR Report Not Critical of U.S.) ///
>         h2(| White-Male | Mixed-Male | White-Mixed | Mixed-Mixed) append

Table output written to: tableA3.csv

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
female,%,%,%,%,%
0,44.32,51.33,49.48,46.41,47.75
1,55.68,48.67,50.52,53.59,52.25

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
race,%,%,%,%,%
1,63.24,63.76,64.77,66.48,64.59
2,16.22,16.11,18.65,16.76,17.00
3,11.89,17.45,12.44,12.85,13.46
4,8.65,2.68,4.15,3.91,4.96

. tabout age panel using "tableA3.csv" if country==1 & outcome==1, ptotal(none) ///
>         cells(col) style(csv) format(2)  h1(UNHCR Report Not Critical of U.S.) ///
>         h2(| White-Male | Mixed-Male | White-Mixed | Mixed-Mixed) append

Table output written to: tableA3.csv

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
female,%,%,%,%,%
0,44.32,51.33,49.48,46.41,47.75
1,55.68,48.67,50.52,53.59,52.25

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
race,%,%,%,%,%
1,63.24,63.76,64.77,66.48,64.59
2,16.22,16.11,18.65,16.76,17.00
3,11.89,17.45,12.44,12.85,13.46
4,8.65,2.68,4.15,3.91,4.96

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
age,%,%,%,%,%
1,18.38,16.00,16.49,12.71,15.92
2,12.43,16.67,18.56,22.10,17.46
3,17.84,18.67,16.49,19.34,18.03
4,16.22,12.67,14.95,14.92,14.79
5,16.76,18.67,12.89,14.36,15.49
6,18.38,17.33,20.62,16.57,18.31

. tabout party panel using "tableA3.csv" if country==1 & outcome==1, ptotal(none) ///
>         cells(col) style(csv) format(2)  h1(UNHCR Report Not Critical of U.S.) ///
>         h2(| White-Male | Mixed-Male | White-Mixed | Mixed-Mixed) append

Table output written to: tableA3.csv

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
female,%,%,%,%,%
0,44.32,51.33,49.48,46.41,47.75
1,55.68,48.67,50.52,53.59,52.25

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
race,%,%,%,%,%
1,63.24,63.76,64.77,66.48,64.59
2,16.22,16.11,18.65,16.76,17.00
3,11.89,17.45,12.44,12.85,13.46
4,8.65,2.68,4.15,3.91,4.96

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
age,%,%,%,%,%
1,18.38,16.00,16.49,12.71,15.92
2,12.43,16.67,18.56,22.10,17.46
3,17.84,18.67,16.49,19.34,18.03
4,16.22,12.67,14.95,14.92,14.79
5,16.76,18.67,12.89,14.36,15.49
6,18.38,17.33,20.62,16.57,18.31

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
party,%,%,%,%,%
1,32.97,32.67,38.14,27.62,32.96
2,40.00,34.67,30.93,39.23,36.20
3,27.03,32.67,30.93,33.15,30.85

. tabout female panel using "tableA3.csv" if country==1 & outcome==2, ptotal(none) ///
>         cells(col) style(csv) format(2)  h1(UNHCR Report Critical of U.S.) ///
>         h2(| White-Male | Mixed-Male | White-Mixed | Mixed-Mixed) append

Table output written to: tableA3.csv

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
female,%,%,%,%,%
0,44.32,51.33,49.48,46.41,47.75
1,55.68,48.67,50.52,53.59,52.25

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
race,%,%,%,%,%
1,63.24,63.76,64.77,66.48,64.59
2,16.22,16.11,18.65,16.76,17.00
3,11.89,17.45,12.44,12.85,13.46
4,8.65,2.68,4.15,3.91,4.96

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
age,%,%,%,%,%
1,18.38,16.00,16.49,12.71,15.92
2,12.43,16.67,18.56,22.10,17.46
3,17.84,18.67,16.49,19.34,18.03
4,16.22,12.67,14.95,14.92,14.79
5,16.76,18.67,12.89,14.36,15.49
6,18.38,17.33,20.62,16.57,18.31

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
party,%,%,%,%,%
1,32.97,32.67,38.14,27.62,32.96
2,40.00,34.67,30.93,39.23,36.20
3,27.03,32.67,30.93,33.15,30.85

UNHCR Report Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
female,%,%,%,%,%
0,44.05,46.24,49.18,47.51,46.80
1,55.95,53.76,50.82,52.49,53.20

. tabout race panel using "tableA3.csv" if country==1 & outcome==2, ptotal(none) ///
>         cells(col) style(csv) format(2)  h1(UNHCR Report Critical of U.S.) ///
>         h2(| White-Male | Mixed-Male | White-Mixed | Mixed-Mixed) append

Table output written to: tableA3.csv

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
female,%,%,%,%,%
0,44.32,51.33,49.48,46.41,47.75
1,55.68,48.67,50.52,53.59,52.25

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
race,%,%,%,%,%
1,63.24,63.76,64.77,66.48,64.59
2,16.22,16.11,18.65,16.76,17.00
3,11.89,17.45,12.44,12.85,13.46
4,8.65,2.68,4.15,3.91,4.96

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
age,%,%,%,%,%
1,18.38,16.00,16.49,12.71,15.92
2,12.43,16.67,18.56,22.10,17.46
3,17.84,18.67,16.49,19.34,18.03
4,16.22,12.67,14.95,14.92,14.79
5,16.76,18.67,12.89,14.36,15.49
6,18.38,17.33,20.62,16.57,18.31

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
party,%,%,%,%,%
1,32.97,32.67,38.14,27.62,32.96
2,40.00,34.67,30.93,39.23,36.20
3,27.03,32.67,30.93,33.15,30.85

UNHCR Report Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
female,%,%,%,%,%
0,44.05,46.24,49.18,47.51,46.80
1,55.95,53.76,50.82,52.49,53.20

UNHCR Report Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
race,%,%,%,%,%
1,72.29,61.08,67.96,63.33,66.01
2,11.45,16.22,13.81,17.22,14.75
3,12.05,17.84,12.15,13.33,13.90
4,4.22,4.86,6.08,6.11,5.34

. tabout age panel using "tableA3.csv" if country==1 & outcome==2, ptotal(none) ///
>         cells(col) style(csv) format(2)  h1(UNHCR Report Critical of U.S.) ///
>         h2(| White-Male | Mixed-Male | White-Mixed | Mixed-Mixed) append

Table output written to: tableA3.csv

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
female,%,%,%,%,%
0,44.32,51.33,49.48,46.41,47.75
1,55.68,48.67,50.52,53.59,52.25

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
race,%,%,%,%,%
1,63.24,63.76,64.77,66.48,64.59
2,16.22,16.11,18.65,16.76,17.00
3,11.89,17.45,12.44,12.85,13.46
4,8.65,2.68,4.15,3.91,4.96

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
age,%,%,%,%,%
1,18.38,16.00,16.49,12.71,15.92
2,12.43,16.67,18.56,22.10,17.46
3,17.84,18.67,16.49,19.34,18.03
4,16.22,12.67,14.95,14.92,14.79
5,16.76,18.67,12.89,14.36,15.49
6,18.38,17.33,20.62,16.57,18.31

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
party,%,%,%,%,%
1,32.97,32.67,38.14,27.62,32.96
2,40.00,34.67,30.93,39.23,36.20
3,27.03,32.67,30.93,33.15,30.85

UNHCR Report Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
female,%,%,%,%,%
0,44.05,46.24,49.18,47.51,46.80
1,55.95,53.76,50.82,52.49,53.20

UNHCR Report Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
race,%,%,%,%,%
1,72.29,61.08,67.96,63.33,66.01
2,11.45,16.22,13.81,17.22,14.75
3,12.05,17.84,12.15,13.33,13.90
4,4.22,4.86,6.08,6.11,5.34

UNHCR Report Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
age,%,%,%,%,%
1,11.31,14.52,10.93,16.02,13.23
2,14.29,18.28,21.86,19.34,18.52
3,19.05,19.35,14.21,17.13,17.41
4,22.02,17.20,15.85,12.15,16.71
5,15.48,12.90,16.39,13.81,14.62
6,17.86,17.74,20.77,21.55,19.50

. tabout party panel using "tableA3.csv" if country==1 & outcome==2, ptotal(none) ///
>         cells(col) style(csv) format(2)  h1(UNHCR Report Critical of U.S.) ///
>         h2(| White-Male | Mixed-Male | White-Mixed | Mixed-Mixed) append

Table output written to: tableA3.csv

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
female,%,%,%,%,%
0,44.32,51.33,49.48,46.41,47.75
1,55.68,48.67,50.52,53.59,52.25

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
race,%,%,%,%,%
1,63.24,63.76,64.77,66.48,64.59
2,16.22,16.11,18.65,16.76,17.00
3,11.89,17.45,12.44,12.85,13.46
4,8.65,2.68,4.15,3.91,4.96

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
age,%,%,%,%,%
1,18.38,16.00,16.49,12.71,15.92
2,12.43,16.67,18.56,22.10,17.46
3,17.84,18.67,16.49,19.34,18.03
4,16.22,12.67,14.95,14.92,14.79
5,16.76,18.67,12.89,14.36,15.49
6,18.38,17.33,20.62,16.57,18.31

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
party,%,%,%,%,%
1,32.97,32.67,38.14,27.62,32.96
2,40.00,34.67,30.93,39.23,36.20
3,27.03,32.67,30.93,33.15,30.85

UNHCR Report Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
female,%,%,%,%,%
0,44.05,46.24,49.18,47.51,46.80
1,55.95,53.76,50.82,52.49,53.20

UNHCR Report Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
race,%,%,%,%,%
1,72.29,61.08,67.96,63.33,66.01
2,11.45,16.22,13.81,17.22,14.75
3,12.05,17.84,12.15,13.33,13.90
4,4.22,4.86,6.08,6.11,5.34

UNHCR Report Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
age,%,%,%,%,%
1,11.31,14.52,10.93,16.02,13.23
2,14.29,18.28,21.86,19.34,18.52
3,19.05,19.35,14.21,17.13,17.41
4,22.02,17.20,15.85,12.15,16.71
5,15.48,12.90,16.39,13.81,14.62
6,17.86,17.74,20.77,21.55,19.50

UNHCR Report Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
party,%,%,%,%,%
1,27.98,31.72,38.80,29.83,32.17
2,34.52,36.02,37.70,34.81,35.79
3,37.50,32.26,23.50,35.36,32.03

. 
. *** Table A4 Sample Treatment Balance (w/ country labels)
. tabout female panel using "tableA4.csv" if country==2 & outcome==1, ptotal(none) ///
>         cells(col) style(csv) format(2) h1(UNHCR Report Not Critical of U.S.) ///
>         h2(| White-Male | Mixed-Male | White-Mixed | Mixed-Mixed) replace

Table output written to: tableA4.csv

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
female,%,%,%,%,%
0,51.23,49.50,48.60,51.06,50.13
1,48.77,50.50,51.40,48.94,49.87

. tabout race panel using "tableA4.csv" if country==2 & outcome==1, ptotal(none) ///
>         cells(col) style(csv) format(2)  h1(UNHCR Report Not Critical of U.S.) ///
>         h2(| White-Male | Mixed-Male | White-Mixed | Mixed-Mixed) append

Table output written to: tableA4.csv

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
female,%,%,%,%,%
0,51.23,49.50,48.60,51.06,50.13
1,48.77,50.50,51.40,48.94,49.87

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
race,%,%,%,%,%
1,65.52,67.82,66.48,66.13,66.49
2,17.24,15.35,16.48,13.98,15.78
3,10.84,12.38,10.23,13.44,11.73
4,6.40,4.46,6.82,6.45,6.00

. tabout age panel using "tableA4.csv" if country==2 & outcome==1, ptotal(none) ///
>         cells(col) style(csv) format(2)  h1(UNHCR Report Not Critical of U.S.) ///
>         h2(| White-Male | Mixed-Male | White-Mixed | Mixed-Mixed) append

Table output written to: tableA4.csv

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
female,%,%,%,%,%
0,51.23,49.50,48.60,51.06,50.13
1,48.77,50.50,51.40,48.94,49.87

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
race,%,%,%,%,%
1,65.52,67.82,66.48,66.13,66.49
2,17.24,15.35,16.48,13.98,15.78
3,10.84,12.38,10.23,13.44,11.73
4,6.40,4.46,6.82,6.45,6.00

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
age,%,%,%,%,%
1,14.29,12.38,13.41,11.17,12.82
2,21.67,18.32,19.55,23.94,20.85
3,19.21,15.35,16.76,13.83,16.32
4,14.29,19.31,17.88,16.49,16.97
5,14.29,18.32,15.64,17.55,16.45
6,16.26,16.34,16.76,17.02,16.58

. tabout party panel using "tableA4.csv" if country==2 & outcome==1, ptotal(none) ///
>         cells(col) style(csv) format(2)  h1(UNHCR Report Not Critical of U.S.) ///
>         h2(| White-Male | Mixed-Male | White-Mixed | Mixed-Mixed) append

Table output written to: tableA4.csv

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
female,%,%,%,%,%
0,51.23,49.50,48.60,51.06,50.13
1,48.77,50.50,51.40,48.94,49.87

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
race,%,%,%,%,%
1,65.52,67.82,66.48,66.13,66.49
2,17.24,15.35,16.48,13.98,15.78
3,10.84,12.38,10.23,13.44,11.73
4,6.40,4.46,6.82,6.45,6.00

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
age,%,%,%,%,%
1,14.29,12.38,13.41,11.17,12.82
2,21.67,18.32,19.55,23.94,20.85
3,19.21,15.35,16.76,13.83,16.32
4,14.29,19.31,17.88,16.49,16.97
5,14.29,18.32,15.64,17.55,16.45
6,16.26,16.34,16.76,17.02,16.58

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
party,%,%,%,%,%
1,33.50,35.64,29.05,38.30,34.20
2,33.99,35.64,35.75,42.02,36.79
3,32.51,28.71,35.20,19.68,29.02

. tabout female panel using "tableA4.csv" if country==2 & outcome==2, ptotal(none) ///
>         cells(col) style(csv) format(2)  h1(UNHCR Report Critical of U.S.) ///
>         h2(| White-Male | Mixed-Male | White-Mixed | Mixed-Mixed) append

Table output written to: tableA4.csv

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
female,%,%,%,%,%
0,51.23,49.50,48.60,51.06,50.13
1,48.77,50.50,51.40,48.94,49.87

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
race,%,%,%,%,%
1,65.52,67.82,66.48,66.13,66.49
2,17.24,15.35,16.48,13.98,15.78
3,10.84,12.38,10.23,13.44,11.73
4,6.40,4.46,6.82,6.45,6.00

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
age,%,%,%,%,%
1,14.29,12.38,13.41,11.17,12.82
2,21.67,18.32,19.55,23.94,20.85
3,19.21,15.35,16.76,13.83,16.32
4,14.29,19.31,17.88,16.49,16.97
5,14.29,18.32,15.64,17.55,16.45
6,16.26,16.34,16.76,17.02,16.58

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
party,%,%,%,%,%
1,33.50,35.64,29.05,38.30,34.20
2,33.99,35.64,35.75,42.02,36.79
3,32.51,28.71,35.20,19.68,29.02

UNHCR Report Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
female,%,%,%,%,%
0,46.67,48.26,49.74,55.21,49.86
1,53.33,51.74,50.26,44.79,50.14

. tabout race panel using "tableA4.csv" if country==2 & outcome==2, ptotal(none) ///
>         cells(col) style(csv) format(2)  h1(UNHCR Report Critical of U.S.) ///
>         h2(| White-Male | Mixed-Male | White-Mixed | Mixed-Mixed) append

Table output written to: tableA4.csv

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
female,%,%,%,%,%
0,51.23,49.50,48.60,51.06,50.13
1,48.77,50.50,51.40,48.94,49.87

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
race,%,%,%,%,%
1,65.52,67.82,66.48,66.13,66.49
2,17.24,15.35,16.48,13.98,15.78
3,10.84,12.38,10.23,13.44,11.73
4,6.40,4.46,6.82,6.45,6.00

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
age,%,%,%,%,%
1,14.29,12.38,13.41,11.17,12.82
2,21.67,18.32,19.55,23.94,20.85
3,19.21,15.35,16.76,13.83,16.32
4,14.29,19.31,17.88,16.49,16.97
5,14.29,18.32,15.64,17.55,16.45
6,16.26,16.34,16.76,17.02,16.58

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
party,%,%,%,%,%
1,33.50,35.64,29.05,38.30,34.20
2,33.99,35.64,35.75,42.02,36.79
3,32.51,28.71,35.20,19.68,29.02

UNHCR Report Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
female,%,%,%,%,%
0,46.67,48.26,49.74,55.21,49.86
1,53.33,51.74,50.26,44.79,50.14

UNHCR Report Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
race,%,%,%,%,%
1,64.25,68.02,67.55,71.17,67.66
2,16.76,15.12,13.30,12.88,14.53
3,13.41,13.37,13.30,9.82,12.54
4,5.59,3.49,5.85,6.13,5.27

. tabout age panel using "tableA4.csv" if country==2 & outcome==2, ptotal(none) ///
>         cells(col) style(csv) format(2)  h1(UNHCR Report Critical of U.S.) ///
>         h2(| White-Male | Mixed-Male | White-Mixed | Mixed-Mixed) append

Table output written to: tableA4.csv

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
female,%,%,%,%,%
0,51.23,49.50,48.60,51.06,50.13
1,48.77,50.50,51.40,48.94,49.87

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
race,%,%,%,%,%
1,65.52,67.82,66.48,66.13,66.49
2,17.24,15.35,16.48,13.98,15.78
3,10.84,12.38,10.23,13.44,11.73
4,6.40,4.46,6.82,6.45,6.00

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
age,%,%,%,%,%
1,14.29,12.38,13.41,11.17,12.82
2,21.67,18.32,19.55,23.94,20.85
3,19.21,15.35,16.76,13.83,16.32
4,14.29,19.31,17.88,16.49,16.97
5,14.29,18.32,15.64,17.55,16.45
6,16.26,16.34,16.76,17.02,16.58

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
party,%,%,%,%,%
1,33.50,35.64,29.05,38.30,34.20
2,33.99,35.64,35.75,42.02,36.79
3,32.51,28.71,35.20,19.68,29.02

UNHCR Report Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
female,%,%,%,%,%
0,46.67,48.26,49.74,55.21,49.86
1,53.33,51.74,50.26,44.79,50.14

UNHCR Report Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
race,%,%,%,%,%
1,64.25,68.02,67.55,71.17,67.66
2,16.76,15.12,13.30,12.88,14.53
3,13.41,13.37,13.30,9.82,12.54
4,5.59,3.49,5.85,6.13,5.27

UNHCR Report Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
age,%,%,%,%,%
1,15.56,15.12,13.61,9.20,13.46
2,18.33,18.02,23.56,23.31,20.82
3,12.22,18.60,9.95,17.79,14.45
4,15.00,18.02,18.85,14.11,16.57
5,16.11,15.12,14.66,24.54,17.42
6,22.78,15.12,19.37,11.04,17.28

. tabout party panel using "tableA4.csv" if country==2 & outcome==2, ptotal(none) ///
>         cells(col) style(csv) format(2)  h1(UNHCR Report Critical of U.S.) ///
>         h2(| White-Male | Mixed-Male | White-Mixed | Mixed-Mixed) append

Table output written to: tableA4.csv

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
female,%,%,%,%,%
0,51.23,49.50,48.60,51.06,50.13
1,48.77,50.50,51.40,48.94,49.87

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
race,%,%,%,%,%
1,65.52,67.82,66.48,66.13,66.49
2,17.24,15.35,16.48,13.98,15.78
3,10.84,12.38,10.23,13.44,11.73
4,6.40,4.46,6.82,6.45,6.00

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
age,%,%,%,%,%
1,14.29,12.38,13.41,11.17,12.82
2,21.67,18.32,19.55,23.94,20.85
3,19.21,15.35,16.76,13.83,16.32
4,14.29,19.31,17.88,16.49,16.97
5,14.29,18.32,15.64,17.55,16.45
6,16.26,16.34,16.76,17.02,16.58

UNHCR Report Not Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
party,%,%,%,%,%
1,33.50,35.64,29.05,38.30,34.20
2,33.99,35.64,35.75,42.02,36.79
3,32.51,28.71,35.20,19.68,29.02

UNHCR Report Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
female,%,%,%,%,%
0,46.67,48.26,49.74,55.21,49.86
1,53.33,51.74,50.26,44.79,50.14

UNHCR Report Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
race,%,%,%,%,%
1,64.25,68.02,67.55,71.17,67.66
2,16.76,15.12,13.30,12.88,14.53
3,13.41,13.37,13.30,9.82,12.54
4,5.59,3.49,5.85,6.13,5.27

UNHCR Report Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
age,%,%,%,%,%
1,15.56,15.12,13.61,9.20,13.46
2,18.33,18.02,23.56,23.31,20.82
3,12.22,18.60,9.95,17.79,14.45
4,15.00,18.02,18.85,14.11,16.57
5,16.11,15.12,14.66,24.54,17.42
6,22.78,15.12,19.37,11.04,17.28

UNHCR Report Critical of U.S.
, White-Male , Mixed-Male , White-Mixed , Mixed-Mixed
party,%,%,%,%,%
1,37.22,31.40,34.55,36.20,34.84
2,26.11,38.37,31.94,31.29,31.87
3,36.67,30.23,33.51,32.52,33.29

. 
. ********************************************************************************
. *********************** Appendix A4 ********************************************
. ********************************************************************************
. use "CH_main.dta", clear

. 
. // recalculate factor and irt scales
. factor imm1 imm2_1 imm2_2 imm3, pcf
(obs=2,517)

Factor analysis/correlation                      Number of obs    =      2,517
    Method: principal-component factors          Retained factors =          1
    Rotation: (unrotated)                        Number of params =          4

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |      2.59175      1.95401            0.6479       0.6479
        Factor2  |      0.63774      0.11203            0.1594       0.8074
        Factor3  |      0.52571      0.28090            0.1314       0.9388
        Factor4  |      0.24481            .            0.0612       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(6)  = 3892.44 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    ---------------------------------------
        Variable |  Factor1 |   Uniqueness 
    -------------+----------+--------------
            imm1 |   0.7690 |      0.4086  
          imm2_1 |   0.8615 |      0.2578  
          imm2_2 |   0.8669 |      0.2485  
            imm3 |   0.7118 |      0.4934  
    ---------------------------------------

.                 predict immigration
(regression scoring assumed)

Scoring coefficients (method = regression)

    ------------------------
        Variable |  Factor1 
    -------------+----------
            imm1 |  0.29672 
          imm2_1 |  0.33240 
          imm2_2 |  0.33449 
            imm3 |  0.27462 
    ------------------------


. irt grm imm1 imm2_1 imm2_2 imm3

Fitting fixed-effects model:

Iteration 0:   log likelihood = -15301.294  
Iteration 1:   log likelihood = -15301.294  

Fitting full model:

Iteration 0:   log likelihood = -14357.495  
Iteration 1:   log likelihood = -13366.355  
Iteration 2:   log likelihood = -13298.465  
Iteration 3:   log likelihood = -13271.528  
Iteration 4:   log likelihood = -13271.093  
Iteration 5:   log likelihood =  -13271.09  
Iteration 6:   log likelihood =  -13271.09  

Graded response model                           Number of obs     =      2,528
Log likelihood =  -13271.09
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
imm1         |
     Discrim |   1.679896   .0659523    25.47   0.000     1.550632     1.80916
        Diff |
        >=2  |  -1.911726   .0697637   -27.40   0.000    -2.048461   -1.774992
        >=3  |  -.8553684   .0428667   -19.95   0.000    -.9393856   -.7713512
        >=4  |   .5339274   .0378948    14.09   0.000      .459655    .6081999
         =5  |   1.186934   .0507015    23.41   0.000     1.087561    1.286307
-------------+----------------------------------------------------------------
imm2_1       |
     Discrim |   3.596435   .1680964    21.40   0.000     3.266972    3.925898
        Diff |
        >=2  |  -.7326505   .0317797   -23.05   0.000    -.7949375   -.6703635
        >=3  |   .1601078      .0269     5.95   0.000     .1073847    .2128308
        >=4  |   .8224607   .0321508    25.58   0.000     .7594463    .8854751
         =5  |   1.392439   .0426247    32.67   0.000     1.308896    1.475982
-------------+----------------------------------------------------------------
imm2_2       |
     Discrim |   4.337943   .2539303    17.08   0.000     3.840248    4.835637
        Diff |
        >=2  |  -.4912213   .0285159   -17.23   0.000    -.5471114   -.4353311
        >=3  |   .3610225   .0266547    13.54   0.000     .3087802    .4132647
        >=4  |   .9742977    .033451    29.13   0.000     .9087349    1.039861
         =5  |   1.465138   .0432645    33.86   0.000     1.380341    1.549935
-------------+----------------------------------------------------------------
imm3         |
     Discrim |   1.439258   .0592559    24.29   0.000     1.323118    1.555397
        Diff |
        >=2  |  -.9085988   .0491383   -18.49   0.000    -1.004908   -.8122895
        >=3  |   .1571745    .037455     4.20   0.000     .0837641     .230585
        >=4  |   1.007947   .0487844    20.66   0.000     .9123317    1.103563
         =5  |   1.758207   .0711899    24.70   0.000     1.618677    1.897736
------------------------------------------------------------------------------

.                 predict immigration1
(option pr assumed)
(option conditional(ebmeans) assumed)
(using 7 quadrature points)

. factor race1_1 race1_2 race1_3 race1_4 race1_6 race1_8 race2, pcf
(obs=2,509)

Factor analysis/correlation                      Number of obs    =      2,509
    Method: principal-component factors          Retained factors =          1
    Rotation: (unrotated)                        Number of params =          7

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |      4.24615      3.54829            0.6066       0.6066
        Factor2  |      0.69787      0.17229            0.0997       0.7063
        Factor3  |      0.52557      0.03708            0.0751       0.7814
        Factor4  |      0.48850      0.09285            0.0698       0.8512
        Factor5  |      0.39565      0.04606            0.0565       0.9077
        Factor6  |      0.34959      0.05292            0.0499       0.9576
        Factor7  |      0.29667            .            0.0424       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(21) = 8687.12 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    ---------------------------------------
        Variable |  Factor1 |   Uniqueness 
    -------------+----------+--------------
         race1_1 |   0.8615 |      0.2577  
         race1_2 |   0.7166 |      0.4865  
         race1_3 |   0.8144 |      0.3367  
         race1_4 |   0.8067 |      0.3492  
         race1_6 |   0.7190 |      0.4830  
         race1_8 |   0.8090 |      0.3456  
           race2 |   0.7106 |      0.4951  
    ---------------------------------------

.                 predict white_id
(regression scoring assumed)

Scoring coefficients (method = regression)

    ------------------------
        Variable |  Factor1 
    -------------+----------
         race1_1 |  0.20290 
         race1_2 |  0.16875 
         race1_3 |  0.19181 
         race1_4 |  0.18999 
         race1_6 |  0.16933 
         race1_8 |  0.19052 
           race2 |  0.16734 
    ------------------------


. irt grm race1_1 race1_2 race1_3 race1_4 race1_6 race1_8 race2

Fitting fixed-effects model:

Iteration 0:   log likelihood = -26226.749  
Iteration 1:   log likelihood = -26226.749  

Fitting full model:

Iteration 0:   log likelihood = -23636.517  
Iteration 1:   log likelihood = -21673.055  
Iteration 2:   log likelihood = -21603.817  
Iteration 3:   log likelihood = -21599.871  
Iteration 4:   log likelihood = -21599.855  
Iteration 5:   log likelihood = -21599.855  

Graded response model                           Number of obs     =      2,523
Log likelihood = -21599.855
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
race1_1      |
     Discrim |    3.88038    .152367    25.47   0.000     3.581746    4.179014
        Diff |
        >=2  |  -.5060492   .0281229   -17.99   0.000    -.5611689   -.4509294
        >=3  |   .0282052   .0260815     1.08   0.280    -.0229135    .0793239
        >=4  |   .6967418   .0301381    23.12   0.000     .6376723    .7558113
         =5  |    1.72635   .0490032    35.23   0.000     1.630305    1.822395
-------------+----------------------------------------------------------------
race1_2      |
     Discrim |   1.886999   .0676105    27.91   0.000     1.754485    2.019513
        Diff |
        >=2  |  -1.762586   .0620276   -28.42   0.000    -1.884158   -1.641014
        >=3  |  -.8867148     .04076   -21.75   0.000    -.9666028   -.8068267
        >=4  |    .220043   .0330599     6.66   0.000     .1552468    .2848392
         =5  |   1.551798   .0551118    28.16   0.000     1.443781    1.659815
-------------+----------------------------------------------------------------
race1_3      |
     Discrim |   2.801587    .096422    29.06   0.000     2.612604    2.990571
        Diff |
        >=2  |  -.9358541   .0358118   -26.13   0.000    -1.006044   -.8656643
        >=3  |  -.2350935   .0285798    -8.23   0.000    -.2911088   -.1790781
        >=4  |    .554549   .0308349    17.98   0.000     .4941138    .6149842
         =5  |   1.612122   .0493621    32.66   0.000     1.515374     1.70887
-------------+----------------------------------------------------------------
race1_4      |
     Discrim |   2.617925   .0906964    28.86   0.000     2.440164    2.795687
        Diff |
        >=2  |  -.7853555   .0344656   -22.79   0.000    -.8529068   -.7178042
        >=3  |  -.0066236   .0286776    -0.23   0.817    -.0628307    .0495836
        >=4  |   .7888459   .0342219    23.05   0.000     .7217722    .8559197
         =5  |   1.723806   .0533065    32.34   0.000     1.619328    1.828285
-------------+----------------------------------------------------------------
race1_6      |
     Discrim |   1.899766   .0670124    28.35   0.000     1.768425    2.031108
        Diff |
        >=2  |  -1.472176   .0529271   -27.82   0.000    -1.575911    -1.36844
        >=3  |  -.6172409   .0360847   -17.11   0.000    -.6879655   -.5465163
        >=4  |   .4164753   .0344996    12.07   0.000     .3488573    .4840932
         =5  |    1.80158   .0617597    29.17   0.000     1.680534    1.922627
-------------+----------------------------------------------------------------
race1_8      |
     Discrim |   2.634189   .0926616    28.43   0.000     2.452575    2.815802
        Diff |
        >=2  |  -.5730361   .0319339   -17.94   0.000    -.6356254   -.5104468
        >=3  |   .1661737   .0288591     5.76   0.000     .1096109    .2227364
        >=4  |   .9642112   .0369396    26.10   0.000      .891811    1.036612
         =5  |   1.893646   .0579032    32.70   0.000     1.780158    2.007134
-------------+----------------------------------------------------------------
race2        |
     Discrim |   1.902225   .0739181    25.73   0.000     1.757348    2.047102
        Diff |
        >=2  |  -.0417386   .0328756    -1.27   0.204    -.1061735    .0226963
        >=3  |   .9709654   .0420131    23.11   0.000     .8886214     1.05331
        >=4  |   1.558951   .0563417    27.67   0.000     1.448523    1.669379
         =5  |   2.286068   .0819849    27.88   0.000     2.125381    2.446756
------------------------------------------------------------------------------

.                 predict white_id1       
(option pr assumed)
(option conditional(ebmeans) assumed)
(using 7 quadrature points)

. 
. *** Figure A4: FA and IRT Construction of White Nationalism and Anti-Immigration 
. hist immigration, percent scheme(plottig) color(538g) ///
>         xtitle("Anti-Immigration Attitudes") title("(a) Factor Analysis") 
(bin=34, start=-1.6107584, width=.11547234)

.         graph save "imm_hist.gph", replace
(note: file imm_hist.gph not found)
(file imm_hist.gph saved)

. 
. hist white_id, percent scheme(plottig) color(538g) ///
>         xtitle("White Nationalism") title("(c) Factor Analysis") 
(bin=33, start=-1.7162048, width=.12837295)

.         graph save "race_hist.gph", replace
(note: file race_hist.gph not found)
(file race_hist.gph saved)

.         
. hist immigration1, percent scheme(plottig) color(538g) ///
>         xtitle("Anti-Immigration Attitudes") title("(b) Item Response Theory") 
(bin=34, start=.00118363, width=.01274042)

.         graph save "imm_hist1.gph", replace
(note: file imm_hist1.gph not found)
(file imm_hist1.gph saved)

. 
. hist white_id1, percent scheme(plottig) color(538g) ///
>         xtitle("White Nationalism") title("(d) Item Response Theory") 
(bin=34, start=5.089e-06, width=.02932747)

.         graph save "race_hist1.gph", replace               
(note: file race_hist1.gph not found)
(file race_hist1.gph saved)

.         
. graph combine "imm_hist.gph" "imm_hist1.gph" "race_hist.gph" "race_hist1.gph" 
(note:  clockdir by_legend_position not found in scheme, default attributes used)
(note:  clockdir by_legend_position not found in scheme, default attributes used)

. 
. 
. *** Table A5: Trust and Fairness of the UNHCR Decision-Making Process
. gen patriot = pat1+pat2+pat3
(114 missing values generated)

. // setup short variable for all controls        
. global controls inc age i.party i.white i.female c.edu ///
>                                 immigration white_id patriot

.                                 
. // labeling variables
. label var immigration "Anti-immigration"

. label var white_id "White Nationalism"

. label var female "Female"

. label var inc "Household Income"

. label var age "Age"

. label var patriot "Patriotism"

. label var edu "Education"

. label var white "White"

.                                         
. probit fair_b i.panel $controls if outcome==1, robust

Iteration 0:   log pseudolikelihood = -823.72094  
Iteration 1:   log pseudolikelihood = -692.39119  
Iteration 2:   log pseudolikelihood = -692.10213  
Iteration 3:   log pseudolikelihood = -692.10209  

Probit regression                               Number of obs     =      1,213
                                                Wald chi2(13)     =     229.85
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -692.10209               Pseudo R2         =     0.1598

------------------------------------------------------------------------------
             |               Robust
      fair_b |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |   .1271789   .1102895     1.15   0.249    -.0889845    .3433423
          3  |   .0758325   .1102669     0.69   0.492    -.1402867    .2919517
          4  |   .5535977   .1117629     4.95   0.000     .3345464     .772649
             |
         inc |   .0280118   .0118804     2.36   0.018     .0047266     .051297
         age |   .0079734   .0254526     0.31   0.754    -.0419128    .0578597
             |
       party |
          2  |  -.0639811   .0973659    -0.66   0.511    -.2548147    .1268525
          3  |   .4543834    .101263     4.49   0.000     .2559116    .6528552
             |
     1.white |   .2298413   .0934227     2.46   0.014     .0467363    .4129464
    1.female |  -.2251684   .0816262    -2.76   0.006    -.3851528    -.065184
         edu |   .0451123   .0467547     0.96   0.335    -.0465252    .1367498
 immigration |   .1438605   .0482886     2.98   0.003     .0492165    .2385044
    white_id |   .2025913   .0458655     4.42   0.000     .1126966    .2924859
     patriot |   .0558945   .0160962     3.47   0.001     .0243465    .0874425
       _cons |  -1.455588     .21603    -6.74   0.000    -1.878999   -1.032177
------------------------------------------------------------------------------

.         estimates store m1

. probit fair_b i.panel $controls if outcome==2, robust

Iteration 0:   log pseudolikelihood = -819.86148  
Iteration 1:   log pseudolikelihood = -755.17723  
Iteration 2:   log pseudolikelihood = -755.03191  
Iteration 3:   log pseudolikelihood = -755.03191  

Probit regression                               Number of obs     =      1,187
                                                Wald chi2(13)     =     116.04
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -755.03191               Pseudo R2         =     0.0791

------------------------------------------------------------------------------
             |               Robust
      fair_b |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |   .0622326   .1081349     0.58   0.565    -.1497079     .274173
          3  |    .179393   .1079111     1.66   0.096    -.0321088    .3908949
          4  |   .2801665   .1060698     2.64   0.008     .0722735    .4880595
             |
         inc |  -.0053003   .0111924    -0.47   0.636     -.027237    .0166364
         age |   .0203258   .0247716     0.82   0.412    -.0282256    .0688773
             |
       party |
          2  |   .3257297   .0925357     3.52   0.000     .1443631    .5070963
          3  |   .2032145   .1040968     1.95   0.051    -.0008114    .4072404
             |
     1.white |   .1291654   .0893395     1.45   0.148    -.0459367    .3042675
    1.female |   .0004242   .0795412     0.01   0.996    -.1554737     .156322
         edu |   -.007933   .0434389    -0.18   0.855    -.0930717    .0772057
 immigration |  -.2884935   .0470174    -6.14   0.000     -.380646   -.1963411
    white_id |   -.163013   .0475822    -3.43   0.001    -.2562724   -.0697536
     patriot |   .0073997   .0164698     0.45   0.653    -.0248806      .03968
       _cons |  -.5789607   .2112842    -2.74   0.006      -.99307   -.1648513
------------------------------------------------------------------------------

.         estimates store m2      

. probit trust_b i.panel $controls if outcome==1, robust

Iteration 0:   log pseudolikelihood =   -832.574  
Iteration 1:   log pseudolikelihood = -686.06885  
Iteration 2:   log pseudolikelihood = -685.57343  
Iteration 3:   log pseudolikelihood = -685.57326  
Iteration 4:   log pseudolikelihood = -685.57326  

Probit regression                               Number of obs     =      1,213
                                                Wald chi2(13)     =     249.15
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -685.57326               Pseudo R2         =     0.1766

------------------------------------------------------------------------------
             |               Robust
     trust_b |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |    .322528   .1094611     2.95   0.003     .1079883    .5370678
          3  |   .0760317   .1110652     0.68   0.494     -.141652    .2937155
          4  |   .3872262   .1106866     3.50   0.000     .1702845    .6041679
             |
         inc |   .0126353   .0120412     1.05   0.294     -.010965    .0362357
         age |  -.0473208    .025469    -1.86   0.063    -.0972391    .0025974
             |
       party |
          2  |   .0244894   .0964426     0.25   0.800    -.1645346    .2135133
          3  |   .4366067   .1032061     4.23   0.000     .2343264     .638887
             |
     1.white |    .310637   .0919529     3.38   0.001     .1304126    .4908614
    1.female |  -.4569452   .0820817    -5.57   0.000    -.6178225    -.296068
         edu |   .1220737    .047101     2.59   0.010     .0297573      .21439
 immigration |    .176382   .0483767     3.65   0.000     .0815653    .2711986
    white_id |   .2060825    .046143     4.47   0.000     .1156438    .2965212
     patriot |   .0774464   .0160948     4.81   0.000     .0459012    .1089916
       _cons |  -1.415922   .2153313    -6.58   0.000    -1.837963   -.9938802
------------------------------------------------------------------------------

.         estimates store m3

. probit trust_b i.panel $controls if outcome==2, robust

Iteration 0:   log pseudolikelihood = -821.66975  
Iteration 1:   log pseudolikelihood = -759.82215  
Iteration 2:   log pseudolikelihood = -759.68484  
Iteration 3:   log pseudolikelihood = -759.68484  

Probit regression                               Number of obs     =      1,187
                                                Wald chi2(13)     =     114.42
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -759.68484               Pseudo R2         =     0.0754

------------------------------------------------------------------------------
             |               Robust
     trust_b |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |   .1053503   .1070664     0.98   0.325    -.1044961    .3151967
          3  |   .2326023     .10756     2.16   0.031     .0217885     .443416
          4  |   .3326015   .1070535     3.11   0.002     .1227806    .5424225
             |
         inc |   .0078989   .0112988     0.70   0.484    -.0142464    .0300441
         age |   .0094119   .0245921     0.38   0.702    -.0387878    .0576116
             |
       party |
          2  |   .3024026   .0930765     3.25   0.001      .119976    .4848292
          3  |   .1784231   .1026286     1.74   0.082    -.0227253    .3795716
             |
     1.white |  -.0878579   .0888884    -0.99   0.323     -.262076    .0863603
    1.female |  -.0820151   .0787631    -1.04   0.298     -.236388    .0723578
         edu |  -.0412719   .0430813    -0.96   0.338    -.1257096    .0431658
 immigration |  -.2842231    .046672    -6.09   0.000    -.3756985   -.1927478
    white_id |  -.1663484   .0468859    -3.55   0.000    -.2582431   -.0744537
     patriot |   .0405135   .0165404     2.45   0.014     .0080948    .0729321
       _cons |  -.5603095   .2109027    -2.66   0.008    -.9736713   -.1469477
------------------------------------------------------------------------------

.         estimates store m4      

. 
. esttab m1 m2 m3 m4 using "tableA5.csv", pr2 b(2) se(2) obslast ///
>            nobaselevels label replace nogaps ///
>            coeflabels(2.party2 "Democrats" 3.party2 "Republicans" ///
>            2.panel "Mixed-Males" 3.panel "White-Mixed" 4.panel "Mixed-Mixed") 
(note: file tableA5.csv not found)
(output written to tableA5.csv)

. 
. *** Table A6: UNHCR Report Outcome for Refugees and U.S. Citizens
. probit refugee_b i.panel $controls if outcome==1, robust

Iteration 0:   log pseudolikelihood = -838.46742  
Iteration 1:   log pseudolikelihood = -662.64632  
Iteration 2:   log pseudolikelihood = -661.63474  
Iteration 3:   log pseudolikelihood = -661.63399  
Iteration 4:   log pseudolikelihood = -661.63399  

Probit regression                               Number of obs     =      1,213
                                                Wald chi2(13)     =     265.31
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -661.63399               Pseudo R2         =     0.2109

------------------------------------------------------------------------------
             |               Robust
   refugee_b |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |   .1394258   .1120749     1.24   0.213     -.080237    .3590887
          3  |   .0194185   .1131514     0.17   0.864    -.2023541    .2411912
          4  |   .3025642   .1120043     2.70   0.007     .0830397    .5220887
             |
         inc |    .054678   .0121714     4.49   0.000     .0308225    .0785336
         age |  -.0790885   .0261461    -3.02   0.002    -.1303338   -.0278432
             |
       party |
          2  |  -.0378313   .0967624    -0.39   0.696    -.2274821    .1518195
          3  |   .3299415   .1046895     3.15   0.002     .1247538    .5351292
             |
     1.white |   .2524866   .0929425     2.72   0.007     .0703226    .4346506
    1.female |   -.362732   .0836937    -4.33   0.000    -.5267686   -.1986953
         edu |   .0090562   .0475565     0.19   0.849    -.0841528    .1022652
 immigration |   .2349318   .0492479     4.77   0.000     .1384078    .3314558
    white_id |   .2467708   .0460081     5.36   0.000     .1565966     .336945
     patriot |   .1078676   .0163238     6.61   0.000     .0758735    .1398617
       _cons |  -1.462116   .2205171    -6.63   0.000    -1.894322   -1.029911
------------------------------------------------------------------------------

.         estimates store m5

. probit refugee_b i.panel $controls if outcome==2, robust

Iteration 0:   log pseudolikelihood = -770.08568  
Iteration 1:   log pseudolikelihood = -646.26452  
Iteration 2:   log pseudolikelihood = -645.86307  
Iteration 3:   log pseudolikelihood = -645.86298  
Iteration 4:   log pseudolikelihood = -645.86298  

Probit regression                               Number of obs     =      1,187
                                                Wald chi2(13)     =     207.61
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -645.86298               Pseudo R2         =     0.1613

------------------------------------------------------------------------------
             |               Robust
   refugee_b |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |  -.0950083   .1132302    -0.84   0.401    -.3169355    .1269188
          3  |   .0019778   .1149326     0.02   0.986     -.223286    .2272415
          4  |   .1084676   .1155638     0.94   0.348    -.1180333    .3349685
             |
         inc |   .0176156    .012017     1.47   0.143    -.0059372    .0411685
         age |  -.0298776   .0263205    -1.14   0.256    -.0814648    .0217096
             |
       party |
          2  |   .2563561   .1007913     2.54   0.011     .0588088    .4539034
          3  |   .2039347   .1065679     1.91   0.056    -.0049345     .412804
             |
     1.white |   .1150366   .0956711     1.20   0.229    -.0724753    .3025484
    1.female |   .1277433   .0839299     1.52   0.128    -.0367563    .2922428
         edu |  -.0232794   .0457709    -0.51   0.611    -.1129888      .06643
 immigration |   -.474415   .0491836    -9.65   0.000     -.570813    -.378017
    white_id |  -.2111369   .0492529    -4.29   0.000    -.3076708   -.1146031
     patriot |  -.0025762   .0181678    -0.14   0.887    -.0381843     .033032
       _cons |   .1895689   .2260547     0.84   0.402    -.2534901     .632628
------------------------------------------------------------------------------

.         estimates store m6

. probit american_b i.panel $controls if outcome==1, robust

Iteration 0:   log pseudolikelihood = -840.56946  
Iteration 1:   log pseudolikelihood = -650.29186  
Iteration 2:   log pseudolikelihood = -648.68432  
Iteration 3:   log pseudolikelihood = -648.68227  
Iteration 4:   log pseudolikelihood = -648.68227  

Probit regression                               Number of obs     =      1,213
                                                Wald chi2(13)     =     276.29
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -648.68227               Pseudo R2         =     0.2283

------------------------------------------------------------------------------
             |               Robust
  american_b |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |    .252519   .1156715     2.18   0.029     .0258071     .479231
          3  |   .1902734   .1134772     1.68   0.094    -.0321378    .4126846
          4  |   .4649373   .1129823     4.12   0.000      .243496    .6863786
             |
         inc |   .0481063   .0121276     3.97   0.000     .0243366     .071876
         age |  -.0599849    .026295    -2.28   0.023    -.1115222   -.0084476
             |
       party |
          2  |  -.0239795   .0960014    -0.25   0.803    -.2121387    .1641797
          3  |   .4975053   .1078915     4.61   0.000     .2860418    .7089688
             |
     1.white |   .2882802   .0924744     3.12   0.002     .1070337    .4695267
    1.female |  -.3935092   .0840877    -4.68   0.000    -.5583181   -.2287002
         edu |    .035575   .0481279     0.74   0.460    -.0587539    .1299038
 immigration |   .2525047    .050972     4.95   0.000     .1526015     .352408
    white_id |   .2512105   .0464977     5.40   0.000     .1600767    .3423443
     patriot |   .0892393   .0164251     5.43   0.000     .0570466    .1214319
       _cons |  -1.400634   .2180473    -6.42   0.000    -1.827999   -.9732689
------------------------------------------------------------------------------

.         estimates store m7

. probit american_b i.panel $controls if outcome==2, robust

Iteration 0:   log pseudolikelihood =  -796.0264  
Iteration 1:   log pseudolikelihood = -661.91973  
Iteration 2:   log pseudolikelihood = -661.55502  
Iteration 3:   log pseudolikelihood = -661.55494  
Iteration 4:   log pseudolikelihood = -661.55494  

Probit regression                               Number of obs     =      1,187
                                                Wald chi2(13)     =     226.34
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -661.55494               Pseudo R2         =     0.1689

------------------------------------------------------------------------------
             |               Robust
  american_b |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |  -.0295095   .1126705    -0.26   0.793    -.2503396    .1913206
          3  |   .0680666   .1131547     0.60   0.547    -.1537125    .2898456
          4  |   .0985516   .1124445     0.88   0.381    -.1218356    .3189389
             |
         inc |   .0136334   .0118758     1.15   0.251    -.0096427    .0369095
         age |  -.0808762   .0258658    -3.13   0.002    -.1315722   -.0301801
             |
       party |
          2  |   .3940829   .0994125     3.96   0.000      .199238    .5889279
          3  |    .075874   .1057405     0.72   0.473    -.1313735    .2831215
             |
     1.white |   .0958446    .095088     1.01   0.313    -.0905245    .2822138
    1.female |   .0814292   .0833245     0.98   0.328    -.0818838    .2447423
         edu |    .009861   .0452165     0.22   0.827    -.0787618    .0984838
 immigration |  -.3916676   .0488259    -8.02   0.000    -.4873646   -.2959706
    white_id |  -.2525722   .0491003    -5.14   0.000    -.3488071   -.1563374
     patriot |   .0038127   .0180669     0.21   0.833    -.0315978    .0392231
       _cons |   .1207821    .225386     0.54   0.592    -.3209664    .5625305
------------------------------------------------------------------------------

.         estimates store m8                      

.         
. esttab m5 m6 m7 m8 using "tableA6.csv", pr2 b(2) se(2) obslast ///
>            nobaselevels label replace nogaps ///
>            coeflabels(2.party2 "Democrats" 3.party2 "Republicans" ///
>            2.panel "Mixed-Males" 3.panel "White-Mixed" 4.panel "Mixed-Mixed")
(note: file tableA6.csv not found)
(output written to tableA6.csv)

. 
. ********************************************************************************
. *********************** Appendix A5 ********************************************
. ********************************************************************************                
. 
. *** Table A7: Alternative Dependent Variable (Factor Analysis Construction)
. 
. factor fair trust refugee american, ipf factors(2)
(obs=2,554)

Factor analysis/correlation                      Number of obs    =      2,554
    Method: iterated principal factors           Retained factors =          2
    Rotation: (unrotated)                        Number of params =          6

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |      2.86283      2.66656            0.9359       0.9359
        Factor2  |      0.19626      0.19598            0.0642       1.0000
        Factor3  |      0.00029      0.00064            0.0001       1.0001
        Factor4  |     -0.00035            .           -0.0001       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(6)  = 7031.06 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    -------------------------------------------------
        Variable |  Factor1   Factor2 |   Uniqueness 
    -------------+--------------------+--------------
            fair |   0.7480    0.3045 |      0.3478  
           trust |   0.7999    0.1479 |      0.3382  
         refugee |   0.9483   -0.2641 |      0.0310  
        american |   0.8741   -0.1094 |      0.2239  
    -------------------------------------------------

. predict substantive procedural 
(regression scoring assumed)

Scoring coefficients (method = regression)

    ----------------------------------
        Variable |  Factor1   Factor2 
    -------------+--------------------
            fair |  0.18469   0.62142 
           trust |  0.15218   0.43539 
         refugee |  0.60694  -1.09218 
        american |  0.13489   0.14454 
    ----------------------------------


. 
. reg procedural i.panel $controls if outcome==1, robust

Linear regression                               Number of obs     =      1,213
                                                F(13, 1199)       =       3.97
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0466
                                                Root MSE          =     .69781

------------------------------------------------------------------------------
             |               Robust
  procedural |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |    .057613   .0535697     1.08   0.282    -.0474878    .1627138
          3  |    .073715   .0551964     1.34   0.182    -.0345773    .1820073
          4  |   .2775872   .0593807     4.67   0.000     .1610856    .3940888
             |
         inc |  -.0133239   .0061475    -2.17   0.030    -.0253851   -.0012628
         age |   .0460056   .0137368     3.35   0.001     .0190547    .0729565
             |
       party |
          2  |   .0163137   .0506372     0.32   0.747    -.0830337    .1156611
          3  |   .1146083   .0516586     2.22   0.027     .0132571    .2159595
             |
     1.white |   .0115371   .0484593     0.24   0.812    -.0835374    .1066117
    1.female |   .0647143   .0425779     1.52   0.129    -.0188212    .1482497
         edu |   .0310447   .0238199     1.30   0.193    -.0156886     .077778
 immigration |  -.0545919   .0245606    -2.22   0.026    -.1027784   -.0064054
    white_id |   .0323317   .0241428     1.34   0.181    -.0150351    .0796985
     patriot |  -.0186572   .0083746    -2.23   0.026    -.0350876   -.0022268
       _cons |  -.0383089    .107586    -0.36   0.722    -.2493866    .1727689
------------------------------------------------------------------------------

.         estimates store f1

. reg procedural i.panel $controls if outcome==2, robust

Linear regression                               Number of obs     =      1,187
                                                F(13, 1173)       =       4.69
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0561
                                                Root MSE          =     .70693

------------------------------------------------------------------------------
             |               Robust
  procedural |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |   .0863508   .0610187     1.42   0.157    -.0333672    .2060687
          3  |   .2064279   .0584582     3.53   0.000     .0917336    .3211223
          4  |    .151675   .0571936     2.65   0.008     .0394619    .2638881
             |
         inc |  -.0090623   .0060477    -1.50   0.134    -.0209279    .0028033
         age |   .0382746   .0136697     2.80   0.005     .0114547    .0650945
             |
       party |
          2  |   .0445882   .0505908     0.88   0.378    -.0546704    .1438467
          3  |   .0741856   .0569783     1.30   0.193    -.0376052    .1859765
             |
     1.white |  -.0738354    .047521    -1.55   0.121    -.1670711    .0194002
    1.female |  -.0725014   .0439956    -1.65   0.100    -.1588203    .0138175
         edu |   .0168434   .0245658     0.69   0.493    -.0313544    .0650411
 immigration |   .0815515   .0266296     3.06   0.002     .0293045    .1337985
    white_id |   .0339378   .0266217     1.27   0.203    -.0182937    .0861694
     patriot |   .0044964   .0090534     0.50   0.620    -.0132662     .022259
       _cons |  -.3054554   .1207747    -2.53   0.012     -.542414   -.0684968
------------------------------------------------------------------------------

.         estimates store f2

. reg substantive i.panel $controls if outcome==1, robust

Linear regression                               Number of obs     =      1,213
                                                F(13, 1199)       =      63.73
                                                Prob > F          =     0.0000
                                                R-squared         =     0.3718
                                                Root MSE          =     .83126

------------------------------------------------------------------------------
             |               Robust
 substantive |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |    .213749   .0679594     3.15   0.002     .0804164    .3470816
          3  |   .1057797   .0693951     1.52   0.128    -.0303696    .2419289
          4  |   .3357009   .0674667     4.98   0.000      .203335    .4680668
             |
         inc |   .0265943   .0075081     3.54   0.000     .0118639    .0413247
         age |  -.0316721   .0154818    -2.05   0.041    -.0620465   -.0012978
             |
       party |
          2  |  -.1176896   .0601502    -1.96   0.051     -.235701    .0003219
          3  |   .2884851   .0632223     4.56   0.000     .1644465    .4125237
             |
     1.white |   .1711508   .0573104     2.99   0.003     .0587109    .2835907
    1.female |  -.2670516   .0514266    -5.19   0.000    -.3679477   -.1661554
         edu |   .0326078   .0300593     1.08   0.278    -.0263669    .0915825
 immigration |   .2113863   .0323275     6.54   0.000     .1479615    .2748111
    white_id |   .2120534   .0289965     7.31   0.000     .1551639    .2689429
     patriot |   .0693366   .0101676     6.82   0.000     .0493884    .0892848
       _cons |  -1.164514    .135062    -8.62   0.000    -1.429498   -.8995301
------------------------------------------------------------------------------

.         estimates store f3

. reg substantive i.panel $controls if outcome==2, robust

Linear regression                               Number of obs     =      1,187
                                                F(13, 1173)       =      27.82
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2646
                                                Root MSE          =     .75823

------------------------------------------------------------------------------
             |               Robust
 substantive |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |   .0005399   .0618277     0.01   0.993    -.1207654    .1218452
          3  |   .0265885    .063643     0.42   0.676    -.0982782    .1514553
          4  |   .1221049   .0621813     1.96   0.050     .0001059    .2441039
             |
         inc |   .0011066   .0062252     0.18   0.859    -.0111071    .0133203
         age |  -.0123502   .0137518    -0.90   0.369     -.039331    .0146306
             |
       party |
          2  |   .1892388   .0539328     3.51   0.000     .0834233    .2950543
          3  |   .0455823   .0609775     0.75   0.455    -.0740547    .1652194
             |
     1.white |   .0468192   .0517752     0.90   0.366    -.0547631    .1484014
    1.female |   .0720305   .0436551     1.65   0.099    -.0136203    .1576814
         edu |  -.0249881   .0243837    -1.02   0.306    -.0728286    .0228524
 immigration |  -.3130287    .029958   -10.45   0.000    -.3718059   -.2542514
    white_id |  -.1654646    .029812    -5.55   0.000    -.2239554   -.1069738
     patriot |   .0063762   .0102527     0.62   0.534    -.0137395    .0264919
       _cons |   .0219665   .1169807     0.19   0.851    -.2075483    .2514812
------------------------------------------------------------------------------

.         estimates store f4              

.         
. esttab f1 f2 f3 f4 using "tableA7.csv", r2 b(2) se(2) obslast ///
>            nobaselevels label replace nogaps ///
>            coeflabels(2.party2 "Democrats" 3.party2 "Republicans" ///
>            2.panel "Mixed-Males" 3.panel "White-Mixed" 4.panel "Mixed-Mixed")
(note: file tableA7.csv not found)
(output written to tableA7.csv)

. 
. *** Table A8: Descriptive Representation Effect on Substantive and Procedural Legitimacy
. 
. reg refugee i.panel $controls if outcome==1, robust

Linear regression                               Number of obs     =      1,213
                                                F(13, 1199)       =      54.78
                                                Prob > F          =     0.0000
                                                R-squared         =     0.3410
                                                Root MSE          =     1.1853

------------------------------------------------------------------------------
             |               Robust
     refugee |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |   .2585175   .0963119     2.68   0.007     .0695588    .4474761
          3  |   .1027228   .0984878     1.04   0.297    -.0905048    .2959504
          4  |   .3232452   .0968904     3.34   0.001     .1331517    .5133388
             |
         inc |   .0411691   .0106196     3.88   0.000      .020334    .0620042
         age |    -.06155   .0221572    -2.78   0.006    -.1050211   -.0180789
             |
       party |
          2  |  -.1659211   .0861031    -1.93   0.054    -.3348507    .0030084
          3  |   .3298435   .0893269     3.69   0.000     .1545891     .505098
             |
     1.white |     .22222   .0819372     2.71   0.007     .0614638    .3829763
    1.female |  -.3794591   .0729335    -5.20   0.000    -.5225506   -.2363676
         edu |   .0278253   .0421294     0.66   0.509    -.0548302    .1104809
 immigration |    .305867   .0447662     6.83   0.000     .2180383    .3936958
    white_id |   .2684881    .041499     6.47   0.000     .1870694    .3499069
     patriot |   .1006528   .0144931     6.94   0.000      .072218    .1290875
       _cons |   1.955207   .1931145    10.12   0.000     1.576327    2.334087
------------------------------------------------------------------------------

.         estimates store m1

. reg refugee i.panel $controls if outcome==2, robust

Linear regression                               Number of obs     =      1,187
                                                F(13, 1173)       =      27.72
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2611
                                                Root MSE          =     1.0984

------------------------------------------------------------------------------
             |               Robust
     refugee |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |  -.0391888   .0895008    -0.44   0.662    -.2147883    .1364108
          3  |  -.0567048   .0928294    -0.61   0.541     -.238835    .1254253
          4  |    .097253   .0903866     1.08   0.282    -.0800845    .2745905
             |
         inc |    .005867   .0091382     0.64   0.521    -.0120621    .0237961
         age |  -.0289798   .0200864    -1.44   0.149    -.0683891    .0104295
             |
       party |
          2  |   .2263776   .0783067     2.89   0.004     .0727407    .3800145
          3  |   .0430125   .0890035     0.48   0.629    -.1316113    .2176363
             |
     1.white |   .0993686   .0748161     1.33   0.184    -.0474197     .246157
    1.female |   .1289123    .063974     2.02   0.044     .0033961    .2544285
         edu |  -.0408992   .0359053    -1.14   0.255    -.1113451    .0295466
 immigration |  -.4533944   .0437152   -10.37   0.000    -.5391631   -.3676257
    white_id |  -.2299886   .0432427    -5.32   0.000    -.3148302    -.145147
     patriot |   .0057398   .0146485     0.39   0.695    -.0230003    .0344799
       _cons |   3.653734   .1692288    21.59   0.000     3.321709    3.985758
------------------------------------------------------------------------------

.         estimates store m2

. reg american i.panel $controls if outcome==1, robust

Linear regression                               Number of obs     =      1,213
                                                F(13, 1199)       =      58.26
                                                Prob > F          =     0.0000
                                                R-squared         =     0.3431
                                                Root MSE          =     1.1862

------------------------------------------------------------------------------
             |               Robust
    american |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |   .2471913   .0978885     2.53   0.012     .0551395    .4392432
          3  |    .175555   .0982062     1.79   0.074    -.0171201    .3682301
          4  |   .4127966   .0962433     4.29   0.000     .2239726    .6016206
             |
         inc |   .0389717   .0102751     3.79   0.000     .0188125     .059131
         age |  -.0535111   .0222556    -2.40   0.016    -.0971755   -.0098468
             |
       party |
          2  |  -.1658768   .0858098    -1.93   0.053    -.3342308    .0024773
          3  |   .3960876   .0898467     4.41   0.000     .2198134    .5723618
             |
     1.white |   .2190088   .0812438     2.70   0.007     .0596128    .3784047
    1.female |  -.3885391   .0728416    -5.33   0.000    -.5314502    -.245628
         edu |   .0490026   .0423364     1.16   0.247    -.0340591    .1320643
 immigration |   .2857315   .0451828     6.32   0.000     .1970853    .3743777
    white_id |    .278088    .040614     6.85   0.000     .1984056    .3577705
     patriot |   .0917934   .0145475     6.31   0.000     .0632521    .1203347
       _cons |    2.06933   .1907673    10.85   0.000     1.695055    2.443605
------------------------------------------------------------------------------

.         estimates store m3

. reg american i.panel $controls if outcome==2, robust

Linear regression                               Number of obs     =      1,187
                                                F(13, 1173)       =      36.76
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2998
                                                Root MSE          =     1.1355

------------------------------------------------------------------------------
             |               Robust
    american |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |  -.0051867   .0929953    -0.06   0.956    -.1876423    .1772689
          3  |  -.0063969   .0929688    -0.07   0.945    -.1888006    .1760068
          4  |   .0962076    .092162     1.04   0.297    -.0846132    .2770285
             |
         inc |  -.0009082   .0095403    -0.10   0.924    -.0196261    .0178097
         age |  -.0710588   .0210483    -3.38   0.001    -.1123554   -.0297623
             |
       party |
          2  |   .3108091   .0796743     3.90   0.000      .154489    .4671292
          3  |  -.1002691   .0954628    -1.05   0.294     -.287566    .0870278
             |
     1.white |   .0662498   .0778503     0.85   0.395    -.0864915    .2189911
    1.female |   .1226231   .0678566     1.81   0.071    -.0105107    .2557569
         edu |  -.0332752   .0379599    -0.88   0.381     -.107752    .0412016
 immigration |  -.4350639   .0451668    -9.63   0.000    -.5236806   -.3464472
    white_id |  -.2804681   .0454224    -6.17   0.000    -.3695863     -.19135
     patriot |   .0087004   .0154769     0.56   0.574    -.0216652     .039066
       _cons |    3.68683   .1794983    20.54   0.000     3.334656    4.039004
------------------------------------------------------------------------------

.         estimates store m4      

. reg fair i.panel $controls, robust

Linear regression                               Number of obs     =      2,400
                                                F(13, 2386)       =       8.98
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0479
                                                Root MSE          =     1.2298

------------------------------------------------------------------------------
             |               Robust
        fair |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |   .1052731   .0716706     1.47   0.142      -.03527    .2458161
          3  |   .1253597   .0714733     1.75   0.080    -.0147965    .2655159
          4  |   .3969111   .0708877     5.60   0.000     .2579033    .5359189
             |
         inc |   .0055153   .0075129     0.73   0.463    -.0092173    .0202478
         age |   .0340104   .0163878     2.08   0.038     .0018746    .0661462
             |
       party |
          2  |   .0372057   .0615695     0.60   0.546    -.0835295    .1579409
          3  |   .2644809   .0668056     3.96   0.000     .1334779     .395484
             |
     1.white |    .122958     .05853     2.10   0.036     .0081831    .2377328
    1.female |  -.0356325   .0522105    -0.68   0.495    -.1380151      .06675
         edu |   .0196846   .0299214     0.66   0.511    -.0389901    .0783593
 immigration |  -.0588552   .0323192    -1.82   0.069    -.1222319    .0045215
    white_id |   .0914879   .0304756     3.00   0.003     .0317266    .1512493
     patriot |   .0211614   .0107062     1.98   0.048      .000167    .0421559
       _cons |   2.496012   .1363494    18.31   0.000     2.228636    2.763387
------------------------------------------------------------------------------

.         estimates store m5

. reg trust i.panel $controls, robust

Linear regression                               Number of obs     =      2,400
                                                F(13, 2386)       =      10.44
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0566
                                                Root MSE          =     1.2591

------------------------------------------------------------------------------
             |               Robust
       trust |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |   .2170647   .0744048     2.92   0.004     .0711599    .3629694
          3  |    .157826   .0742465     2.13   0.034     .0122316    .3034203
          4  |   .3976494   .0729015     5.45   0.000     .2546927    .5406062
             |
         inc |   .0101874   .0076465     1.33   0.183    -.0048071    .0251819
         age |   -.000803   .0165055    -0.05   0.961    -.0331697    .0315636
             |
       party |
          2  |   .1037301    .063306     1.64   0.101    -.0204102    .2278705
          3  |   .2220221   .0682239     3.25   0.001     .0882379    .3558063
             |
     1.white |   .0599068   .0606294     0.99   0.323     -.058985    .1787986
    1.female |  -.2068891   .0532408    -3.89   0.000    -.3112922    -.102486
         edu |   .0378865   .0297795     1.27   0.203      -.02051    .0962829
 immigration |  -.0309463    .033734    -0.92   0.359    -.0970973    .0352047
    white_id |   .0522461   .0315247     1.66   0.098    -.0095725    .1140647
     patriot |   .0580789   .0110221     5.27   0.000      .036465    .0796929
       _cons |   2.390339   .1400782    17.06   0.000     2.115651    2.665026
------------------------------------------------------------------------------

.         estimates store m6      

.         
. esttab m1 m2 m3 m4 m5 m6 using "tableA8.csv", r2 b(2) se(2) obslast ///
>            nobaselevels label replace nogaps ///
>            coeflabels(2.party2 "Democrats" 3.party2 "Republicans" ///
>            2.panel "Mixed-Males" 3.panel "White-Mixed" 4.panel "Mixed-Mixed")           
(note: file tableA8.csv not found)
(output written to tableA8.csv)

. 
. ********************************************************************************
. ****************** Appendix A6 *************************************************
. ********************************************************************************
. use "CH_main.dta", clear

. 
. *** Figure A5: UNHCR Panel Diversity and Legitimacy, by Media Attention
. 
. // Media Attention
. reg fair i.panel##c.interest, robust

Linear regression                               Number of obs     =      2,435
                                                F(7, 2427)        =       6.24
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0165
                                                Root MSE          =     1.2484

----------------------------------------------------------------------------------
                 |               Robust
            fair |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
           panel |
              2  |   .4468132    .148515     3.01   0.003     .1555839    .7380425
              3  |   .2934546   .1438127     2.04   0.041     .0114463    .5754629
              4  |   .5420996    .141378     3.83   0.000     .2648655    .8193338
                 |
        interest |   .0797627   .0421374     1.89   0.058    -.0028663    .1623917
                 |
panel#c.interest |
              2  |  -.1387819   .0604829    -2.29   0.022    -.2573854   -.0201784
              3  |  -.0662968    .061254    -1.08   0.279    -.1864124    .0538187
              4  |  -.0580108   .0584873    -0.99   0.321     -.172701    .0566793
                 |
           _cons |   2.862758   .1025276    27.92   0.000     2.661707    3.063809
----------------------------------------------------------------------------------

.         margins r.panel if panel==1 | panel==4, over(interest) post

Contrasts of predictive margins
Model VCE    : Robust

Expression   : Linear prediction, predict()
over         : interest

--------------------------------------------------
               |         df           F        P>F
---------------+----------------------------------
panel@interest |
   (4 vs 1) 0  |          1       14.70     0.0001
   (4 vs 1) 1  |          1       25.88     0.0000
   (4 vs 1) 2  |          1       36.58     0.0000
   (4 vs 1) 3  |          1       17.57     0.0000
   (4 vs 1) 4  |          1        5.56     0.0185
        Joint  |          2       18.42     0.0000
               |
   Denominator |       2427
--------------------------------------------------

----------------------------------------------------------------
               |            Delta-method
               |   Contrast   Std. Err.     [95% Conf. Interval]
---------------+------------------------------------------------
panel@interest |
   (4 vs 1) 0  |   .5420996    .141378      .2648655    .8193338
   (4 vs 1) 1  |   .4840888   .0951538      .2974978    .6706799
   (4 vs 1) 2  |    .426078   .0704433      .2879429    .5642132
   (4 vs 1) 3  |   .3680672   .0878168      .1958636    .5402708
   (4 vs 1) 4  |   .3100564   .1315403      .0521136    .5679993
----------------------------------------------------------------

.         estimates store m1

. reg trust i.panel##c.interest, robust

Linear regression                               Number of obs     =      2,435
                                                F(7, 2427)        =       5.96
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0162
                                                Root MSE          =     1.2842

----------------------------------------------------------------------------------
                 |               Robust
           trust |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
           panel |
              2  |   .4456968   .1513392     2.95   0.003     .1489294    .7424642
              3  |   .2825133   .1508356     1.87   0.061    -.0132665    .5782931
              4  |    .501287   .1488575     3.37   0.001     .2093861    .7931878
                 |
        interest |   .1064782   .0432433     2.46   0.014     .0216805    .1912758
                 |
panel#c.interest |
              2  |  -.0944887   .0614117    -1.54   0.124    -.2149134    .0259361
              3  |  -.0498592   .0624491    -0.80   0.425    -.1723182    .0725999
              4  |  -.0446748   .0603459    -0.74   0.459    -.1630096      .07366
                 |
           _cons |   2.884809   .1074301    26.85   0.000     2.674144    3.095473
----------------------------------------------------------------------------------

.         margins r.panel if panel==1 | panel==4, over(interest) post

Contrasts of predictive margins
Model VCE    : Robust

Expression   : Linear prediction, predict()
over         : interest

--------------------------------------------------
               |         df           F        P>F
---------------+----------------------------------
panel@interest |
   (4 vs 1) 0  |          1       11.34     0.0008
   (4 vs 1) 1  |          1       20.48     0.0000
   (4 vs 1) 2  |          1       30.92     0.0000
   (4 vs 1) 3  |          1       16.70     0.0000
   (4 vs 1) 4  |          1        5.80     0.0161
        Joint  |          2       15.47     0.0000
               |
   Denominator |       2427
--------------------------------------------------

----------------------------------------------------------------
               |            Delta-method
               |   Contrast   Std. Err.     [95% Conf. Interval]
---------------+------------------------------------------------
panel@interest |
   (4 vs 1) 0  |    .501287   .1488575      .2093861    .7931878
   (4 vs 1) 1  |   .4566122   .1009031      .2587471    .6544772
   (4 vs 1) 2  |   .4119374   .0740781      .2666746    .5572002
   (4 vs 1) 3  |   .3672626   .0898719       .191029    .5434961
   (4 vs 1) 4  |   .3225878   .1339761      .0598684    .5853072
----------------------------------------------------------------

.         estimates store m2

. reg american i.panel##c.interest, robust

Linear regression                               Number of obs     =      2,435
                                                F(7, 2427)        =       2.10
                                                Prob > F          =     0.0408
                                                R-squared         =     0.0059
                                                Root MSE          =     1.4064

----------------------------------------------------------------------------------
                 |               Robust
        american |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
           panel |
              2  |    .372093   .1660382     2.24   0.025     .0465018    .6976842
              3  |   .2269359   .1646384     1.38   0.168    -.0959104    .5497822
              4  |   .3452235   .1625614     2.12   0.034       .02645    .6639969
                 |
        interest |    .094076   .0474012     1.98   0.047     .0011251     .187027
                 |
panel#c.interest |
              2  |  -.1233922   .0668105    -1.85   0.065    -.2544037    .0076192
              3  |  -.0883657   .0683286    -1.29   0.196    -.2223541    .0456227
              4  |  -.0542767   .0656184    -0.83   0.408    -.1829505    .0743971
                 |
           _cons |    3.20188   .1183712    27.05   0.000     2.969761    3.433999
----------------------------------------------------------------------------------

.         margins r.panel if panel==1 | panel==4, over(interest) post

Contrasts of predictive margins
Model VCE    : Robust

Expression   : Linear prediction, predict()
over         : interest

--------------------------------------------------
               |         df           F        P>F
---------------+----------------------------------
panel@interest |
   (4 vs 1) 0  |          1        4.51     0.0338
   (4 vs 1) 1  |          1        6.95     0.0084
   (4 vs 1) 2  |          1        8.56     0.0035
   (4 vs 1) 3  |          1        3.50     0.0617
   (4 vs 1) 4  |          1        0.78     0.3779
        Joint  |          2        4.39     0.0125
               |
   Denominator |       2427
--------------------------------------------------

----------------------------------------------------------------
               |            Delta-method
               |   Contrast   Std. Err.     [95% Conf. Interval]
---------------+------------------------------------------------
panel@interest |
   (4 vs 1) 0  |   .3452235   .1625614        .02645    .6639969
   (4 vs 1) 1  |   .2909467   .1103557      .0745456    .5073478
   (4 vs 1) 2  |     .23667   .0808832      .0780628    .3952772
   (4 vs 1) 3  |   .1823932   .0975568     -.0089101    .3736965
   (4 vs 1) 4  |   .1281165   .1452726     -.1567546    .4129876
----------------------------------------------------------------

.         estimates store m3

. reg refugee i.panel##c.interest, robust

Linear regression                               Number of obs     =      2,435
                                                F(7, 2427)        =       2.22
                                                Prob > F          =     0.0298
                                                R-squared         =     0.0061
                                                Root MSE          =     1.3916

----------------------------------------------------------------------------------
                 |               Robust
         refugee |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
           panel |
              2  |   .3009332   .1648009     1.83   0.068    -.0222319    .6240983
              3  |   .0210257   .1639746     0.13   0.898     -.300519    .3425705
              4  |   .2137174   .1630551     1.31   0.190    -.1060242     .533459
                 |
        interest |   .0782797   .0465318     1.68   0.093    -.0129665    .1695258
                 |
panel#c.interest |
              2  |  -.0930538   .0655601    -1.42   0.156    -.2216134    .0355058
              3  |  -.0172018    .067738    -0.25   0.800    -.1500321    .1156286
              4  |    -.01357   .0651567    -0.21   0.835    -.1413386    .1141986
                 |
           _cons |   3.254995   .1177226    27.65   0.000     3.024148    3.485843
----------------------------------------------------------------------------------

.         margins r.panel if panel==1 | panel==4, over(interest) post

Contrasts of predictive margins
Model VCE    : Robust

Expression   : Linear prediction, predict()
over         : interest

--------------------------------------------------
               |         df           F        P>F
---------------+----------------------------------
panel@interest |
   (4 vs 1) 0  |          1        1.72     0.1901
   (4 vs 1) 1  |          1        3.25     0.0714
   (4 vs 1) 2  |          1        5.33     0.0211
   (4 vs 1) 3  |          1        3.24     0.0721
   (4 vs 1) 4  |          1        1.24     0.2649
        Joint  |          2        2.67     0.0693
               |
   Denominator |       2427
--------------------------------------------------

----------------------------------------------------------------
               |            Delta-method
               |   Contrast   Std. Err.     [95% Conf. Interval]
---------------+------------------------------------------------
panel@interest |
   (4 vs 1) 0  |   .2137174   .1630551     -.1060242     .533459
   (4 vs 1) 1  |   .2001474   .1109742     -.0174666    .4177613
   (4 vs 1) 2  |   .1865774   .0808355      .0280636    .3450911
   (4 vs 1) 3  |   .1730074   .0961472     -.0155318    .3615465
   (4 vs 1) 4  |   .1594374    .142986     -.1209499    .4398246
----------------------------------------------------------------

.         estimates store m4

.         
. coefplot m1, bylabel("UNHCR is Fair") || ///
>                  m2, bylabel("UNHCR can be Trusted") || /// 
>                  m3, bylabel("Outcome Good for Americans") || ///
>                  m4, bylabel("Outcome Good for Refugees") format(%9.2g) ///
>                  vertical scheme(plottig) ///
>                  xlabel(1 "1" 2 "2" 3 "3" 4 "4" 5 "5") ///
>                  yline(0) ciopts(recast(rcap)) title("Attention to National Politics") ///
>                  xtitle("Attention to national politics") ///
>                  ytitle("Marginal Difference" "(White-Male vs. Mixed-Mixed)") byopts(rows(1))
(note:  clockdir by_legend_position not found in scheme, default attributes used)

. graph save "media1.gph", replace
(note: file media1.gph not found)
(file media1.gph saved)

. 
. // Media Consumption
. reg fair i.panel##c.media, robust

Linear regression                               Number of obs     =      2,437
                                                F(7, 2429)        =       8.55
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0226
                                                Root MSE          =      1.244

--------------------------------------------------------------------------------
               |               Robust
          fair |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
         panel |
            2  |   .2552322   .1507018     1.69   0.090     -.040285    .5507495
            3  |  -.0051178   .1446872    -0.04   0.972    -.2888408    .2786053
            4  |   .4745918   .1484895     3.20   0.001     .1834126     .765771
               |
        media1 |   .0461002   .0213938     2.15   0.031     .0041482    .0880522
               |
panel#c.media1 |
            2  |  -.0309612   .0300481    -1.03   0.303    -.0898838    .0279613
            3  |   .0316246   .0293263     1.08   0.281    -.0258825    .0891318
            4  |  -.0156696   .0295953    -0.53   0.597    -.0737043    .0423652
               |
         _cons |   2.838048   .1061858    26.73   0.000     2.629824    3.046272
--------------------------------------------------------------------------------

.         margins r.panel if panel==1 | panel==4, over(media1) post

Contrasts of predictive margins
Model VCE    : Robust

Expression   : Linear prediction, predict()
over         : media1

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
panel@media1 |
 (4 vs 1) 0  |          1       10.22     0.0014
 (4 vs 1) 1  |          1       13.86     0.0002
 (4 vs 1) 2  |          1       19.44     0.0000
 (4 vs 1) 3  |          1       27.05     0.0000
 (4 vs 1) 4  |          1       32.84     0.0000
 (4 vs 1) 5  |          1       29.48     0.0000
 (4 vs 1) 6  |          1       20.01     0.0000
 (4 vs 1) 7  |          1       12.22     0.0005
      Joint  |          2       16.52     0.0000
             |
 Denominator |       2429
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
panel@media1 |
 (4 vs 1) 0  |   .4745918   .1484895      .1834126     .765771
 (4 vs 1) 1  |   .4589222   .1232922      .2171534     .700691
 (4 vs 1) 2  |   .4432527   .1005215      .2461358    .6403695
 (4 vs 1) 3  |   .4275831   .0822189      .2663566    .5888096
 (4 vs 1) 4  |   .4119135   .0718825      .2709562    .5528709
 (4 vs 1) 5  |    .396244   .0729795      .2531355    .5393524
 (4 vs 1) 6  |   .3805744   .0850687      .2137597    .5473891
 (4 vs 1) 7  |   .3649049   .1043989      .1601848    .5696249
--------------------------------------------------------------

.         estimates store m5

. reg trust i.panel##c.media, robust

Linear regression                               Number of obs     =      2,437
                                                F(7, 2429)        =       5.87
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0157
                                                Root MSE          =     1.2839

--------------------------------------------------------------------------------
               |               Robust
         trust |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
         panel |
            2  |   .3874066   .1585701     2.44   0.015     .0764599    .6983533
            3  |   .1550241   .1540427     1.01   0.314    -.1470446    .4570928
            4  |   .6520124   .1515174     4.30   0.000     .3548957     .949129
               |
        media1 |   .0429914    .022573     1.90   0.057    -.0012728    .0872556
               |
panel#c.media1 |
            2  |  -.0354322   .0314774    -1.13   0.260    -.0971576    .0262932
            3  |   .0024893   .0306833     0.08   0.935    -.0576788    .0626575
            4  |  -.0555763   .0305427    -1.82   0.069    -.1154688    .0043162
               |
         _cons |   2.936067   .1128668    26.01   0.000     2.714742    3.157392
--------------------------------------------------------------------------------

.         margins r.panel if panel==1 | panel==4, over(media1) post

Contrasts of predictive margins
Model VCE    : Robust

Expression   : Linear prediction, predict()
over         : media1

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
panel@media1 |
 (4 vs 1) 0  |          1       18.52     0.0000
 (4 vs 1) 1  |          1       22.50     0.0000
 (4 vs 1) 2  |          1       27.80     0.0000
 (4 vs 1) 3  |          1       33.15     0.0000
 (4 vs 1) 4  |          1       33.27     0.0000
 (4 vs 1) 5  |          1       23.87     0.0000
 (4 vs 1) 6  |          1       12.62     0.0004
 (4 vs 1) 7  |          1        5.72     0.0168
      Joint  |          2       17.18     0.0000
             |
 Denominator |       2429
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
panel@media1 |
 (4 vs 1) 0  |   .6520124   .1515174      .3548957     .949129
 (4 vs 1) 1  |   .5964361   .1257269       .349893    .8429791
 (4 vs 1) 2  |   .5408598   .1025802       .339706    .7420136
 (4 vs 1) 3  |   .4852835   .0842845      .3200066    .6505604
 (4 vs 1) 4  |   .4297072   .0745034      .2836104     .575804
 (4 vs 1) 5  |   .3741309   .0765726      .2239766    .5242852
 (4 vs 1) 6  |   .3185546   .0896754      .1427065    .4944027
 (4 vs 1) 7  |   .2629783    .109935      .0474022    .4785544
--------------------------------------------------------------

.         estimates store m6

. reg american i.panel##c.media, robust

Linear regression                               Number of obs     =      2,437
                                                F(7, 2429)        =       2.97
                                                Prob > F          =     0.0042
                                                R-squared         =     0.0074
                                                Root MSE          =     1.4054

--------------------------------------------------------------------------------
               |               Robust
      american |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
         panel |
            2  |   .2588262   .1706089     1.52   0.129    -.0757279    .5933802
            3  |  -.0990585   .1635501    -0.61   0.545    -.4197706    .2216535
            4  |   .5124084   .1673344     3.06   0.002     .1842756    .8405412
               |
        media1 |   .0244226   .0243829     1.00   0.317    -.0233908    .0722359
               |
panel#c.media1 |
            2  |  -.0374136   .0340167    -1.10   0.272    -.1041183    .0292912
            3  |   .0277699   .0331199     0.84   0.402    -.0371762    .0927161
            4  |  -.0629774   .0337549    -1.87   0.062    -.1291688    .0032139
               |
         _cons |   3.306697   .1206569    27.41   0.000     3.070095    3.543298
--------------------------------------------------------------------------------

.         margins r.panel if panel==1 | panel==4, over(media1) post

Contrasts of predictive margins
Model VCE    : Robust

Expression   : Linear prediction, predict()
over         : media1

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
panel@media1 |
 (4 vs 1) 0  |          1        9.38     0.0022
 (4 vs 1) 1  |          1       10.50     0.0012
 (4 vs 1) 2  |          1       11.72     0.0006
 (4 vs 1) 3  |          1       12.26     0.0005
 (4 vs 1) 4  |          1       10.28     0.0014
 (4 vs 1) 5  |          1        5.62     0.0179
 (4 vs 1) 6  |          1        1.89     0.1692
 (4 vs 1) 7  |          1        0.35     0.5520
      Joint  |          2        6.15     0.0022
             |
 Denominator |       2429
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
panel@media1 |
 (4 vs 1) 0  |   .5124084   .1673344      .1842756    .8405412
 (4 vs 1) 1  |    .449431   .1386807      .1774863    .7213757
 (4 vs 1) 2  |   .3864536   .1128835      .1650957    .6078114
 (4 vs 1) 3  |   .3234761   .0923677      .1423485    .5046038
 (4 vs 1) 4  |   .2604987   .0812385      .1011948    .4198027
 (4 vs 1) 5  |   .1975213    .083345      .0340867    .3609559
 (4 vs 1) 6  |   .1345439   .0978359     -.0573065    .3263943
 (4 vs 1) 7  |   .0715664   .1203167     -.1643676    .3075005
--------------------------------------------------------------

.         estimates store m7

. reg refugee i.panel##c.media, robust

Linear regression                               Number of obs     =      2,437
                                                F(7, 2429)        =       2.43
                                                Prob > F          =     0.0175
                                                R-squared         =     0.0062
                                                Root MSE          =     1.3916

--------------------------------------------------------------------------------
               |               Robust
       refugee |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
         panel |
            2  |   .2259872   .1659685     1.36   0.173    -.0994673    .5514417
            3  |  -.1772853   .1617939    -1.10   0.273    -.4945536    .1399831
            4  |   .3373945   .1667814     2.02   0.043     .0103459     .664443
               |
        media1 |   .0194528   .0239784     0.81   0.417    -.0275675     .066473
               |
panel#c.media1 |
            2  |   -.029949   .0331126    -0.90   0.366    -.0948808    .0349828
            3  |   .0348652    .032851     1.06   0.289    -.0295536     .099284
            4  |  -.0333207   .0333172    -1.00   0.317    -.0986538    .0320124
               |
         _cons |   3.345745    .118553    28.22   0.000      3.11327    3.578221
--------------------------------------------------------------------------------

.         margins r.panel if panel==1 | panel==4, over(media1) post

Contrasts of predictive margins
Model VCE    : Robust

Expression   : Linear prediction, predict()
over         : media1

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
panel@media1 |
 (4 vs 1) 0  |          1        4.09     0.0432
 (4 vs 1) 1  |          1        4.82     0.0282
 (4 vs 1) 2  |          1        5.74     0.0166
 (4 vs 1) 3  |          1        6.58     0.0104
 (4 vs 1) 4  |          1        6.31     0.0121
 (4 vs 1) 5  |          1        4.26     0.0391
 (4 vs 1) 6  |          1        2.03     0.1544
 (4 vs 1) 7  |          1        0.77     0.3788
      Joint  |          2        3.34     0.0356
             |
 Denominator |       2429
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
panel@media1 |
 (4 vs 1) 0  |   .3373945   .1667814      .0103459     .664443
 (4 vs 1) 1  |   .3040738   .1384987      .0324861    .5756615
 (4 vs 1) 2  |   .2707531   .1129946      .0491773    .4923289
 (4 vs 1) 3  |   .2374325   .0925946      .0558599    .4190051
 (4 vs 1) 4  |   .2041118   .0812393      .0448064    .3634173
 (4 vs 1) 5  |   .1707912   .0827402      .0085424    .3330399
 (4 vs 1) 6  |   .1374705   .0964995     -.0517593    .3267003
 (4 vs 1) 7  |   .1041498    .118315     -.1278588    .3361585
--------------------------------------------------------------

.         estimates store m8      

. 
. coefplot m5, bylabel("UNHCR is Fair") || ///
>                  m6, bylabel("UNHCR can be Trusted") || /// 
>                  m7, bylabel("Outcome Good for Americans") || ///
>                  m8, bylabel("Outcome Good for Refugees") format(%9.2g) ///
>                  vertical scheme(plottig) ///
>                  xlabel(1 "0" 2 "1" 3 "2" 4 "3" 5 "4" 6 "5" 7 "6" 8 "7") ///
>                  yline(0) ciopts(recast(rcap)) title("Political Media Consumption") ///
>                  xtitle("Number of days following political news per week") ///
>                  ytitle("Marginal Difference" "(White-Male vs. Mixed-Mixed)") byopts(rows(1))
(note:  clockdir by_legend_position not found in scheme, default attributes used)

. graph save "media2.gph", replace        
(note: file media2.gph not found)
(file media2.gph saved)

.         
. graph combine "media1.gph" "media2.gph", ycommon rows(2)
(note:  clockdir by_legend_position not found in scheme, default attributes used)
(note:  clockdir by_legend_position not found in scheme, default attributes used)
(note:  clockdir by_legend_position not found in scheme, default attributes used)

.           
. *** Figure A6: UNHCR Panel Diversity and Legitimacy, by Respondent Attentiveness
. reg fair i.panel##i.attention, robust

Linear regression                               Number of obs     =      2,556
                                                F(7, 2548)        =       6.85
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0177
                                                Root MSE          =     1.2422

---------------------------------------------------------------------------------
                |               Robust
           fair |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
          panel |
             2  |  -.0888747   .1381129    -0.64   0.520    -.3596997    .1819503
             3  |  -.0823835   .1364233    -0.60   0.546    -.3498953    .1851283
             4  |   .3302651   .1372947     2.41   0.016     .0610446    .5994855
                |
    1.attention |  -.0687373   .1144542    -0.60   0.548      -.29317    .1556955
                |
panel#attention |
           2 1  |   .2894348   .1606286     1.80   0.072     -.025541    .6044106
           3 1  |   .2895338   .1591284     1.82   0.069    -.0225002    .6015679
           4 1  |   .1146289   .1589791     0.72   0.471    -.1971125    .4263704
                |
          _cons |   3.095541   .0983273    31.48   0.000     2.902732    3.288351
---------------------------------------------------------------------------------

.         margins r.panel, at(attention=0) post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()
at           : attention       =           0

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        0.41     0.5200
   (3 vs 1)  |          1        0.36     0.5460
   (4 vs 1)  |          1        5.79     0.0162
      Joint  |          3        4.26     0.0052
             |
 Denominator |       2548
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |  -.0888747   .1381129     -.3596997    .1819503
   (3 vs 1)  |  -.0823835   .1364233     -.3498953    .1851283
   (4 vs 1)  |   .3302651   .1372947      .0610446    .5994855
--------------------------------------------------------------

.         estimates store fair0

. reg fair i.panel##i.attention, robust

Linear regression                               Number of obs     =      2,556
                                                F(7, 2548)        =       6.85
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0177
                                                Root MSE          =     1.2422

---------------------------------------------------------------------------------
                |               Robust
           fair |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
          panel |
             2  |  -.0888747   .1381129    -0.64   0.520    -.3596997    .1819503
             3  |  -.0823835   .1364233    -0.60   0.546    -.3498953    .1851283
             4  |   .3302651   .1372947     2.41   0.016     .0610446    .5994855
                |
    1.attention |  -.0687373   .1144542    -0.60   0.548      -.29317    .1556955
                |
panel#attention |
           2 1  |   .2894348   .1606286     1.80   0.072     -.025541    .6044106
           3 1  |   .2895338   .1591284     1.82   0.069    -.0225002    .6015679
           4 1  |   .1146289   .1589791     0.72   0.471    -.1971125    .4263704
                |
          _cons |   3.095541   .0983273    31.48   0.000     2.902732    3.288351
---------------------------------------------------------------------------------

.         margins r.panel, at(attention=1) post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()
at           : attention       =           1

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        5.98     0.0145
   (3 vs 1)  |          1        6.39     0.0115
   (4 vs 1)  |          1       30.81     0.0000
      Joint  |          3       10.39     0.0000
             |
 Denominator |       2548
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |   .2005601   .0820144      .0397385    .3613816
   (3 vs 1)  |   .2071503   .0819178      .0465181    .3677826
   (4 vs 1)  |    .444894   .0801532      .2877219     .602066
--------------------------------------------------------------

.         estimates store fair1   

. reg trust i.panel##i.attention, robust

Linear regression                               Number of obs     =      2,554
                                                F(7, 2546)        =       4.88
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0133
                                                Root MSE          =     1.2843

---------------------------------------------------------------------------------
                |               Robust
          trust |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
          panel |
             2  |   .0783995   .1473058     0.53   0.595    -.2104518    .3672509
             3  |   .0924405   .1412421     0.65   0.513    -.1845206    .3694016
             4  |    .367731   .1415295     2.60   0.009     .0902063    .6452557
                |
    1.attention |  -.0988509    .121806    -0.81   0.417    -.3376997     .139998
                |
panel#attention |
           2 1  |   .1824937   .1704065     1.07   0.284    -.1516558    .5166431
           3 1  |   .0695112   .1655225     0.42   0.675    -.2550612    .3940836
           4 1  |   .0492307   .1647633     0.30   0.765     -.273853    .3723144
                |
          _cons |   3.210191   .1040577    30.85   0.000     3.006145    3.414238
---------------------------------------------------------------------------------

.         margins r.panel, at(attention=0) post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()
at           : attention       =           0

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        0.28     0.5946
   (3 vs 1)  |          1        0.43     0.5129
   (4 vs 1)  |          1        6.75     0.0094
      Joint  |          3        2.68     0.0454
             |
 Denominator |       2546
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |   .0783995   .1473058     -.2104518    .3672509
   (3 vs 1)  |   .0924405   .1412421     -.1845206    .3694016
   (4 vs 1)  |    .367731   .1415295      .0902063    .6452557
--------------------------------------------------------------

.         estimates store trust0

. reg trust i.panel##i.attention, robust

Linear regression                               Number of obs     =      2,554
                                                F(7, 2546)        =       4.88
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0133
                                                Root MSE          =     1.2843

---------------------------------------------------------------------------------
                |               Robust
          trust |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
          panel |
             2  |   .0783995   .1473058     0.53   0.595    -.2104518    .3672509
             3  |   .0924405   .1412421     0.65   0.513    -.1845206    .3694016
             4  |    .367731   .1415295     2.60   0.009     .0902063    .6452557
                |
    1.attention |  -.0988509    .121806    -0.81   0.417    -.3376997     .139998
                |
panel#attention |
           2 1  |   .1824937   .1704065     1.07   0.284    -.1516558    .5166431
           3 1  |   .0695112   .1655225     0.42   0.675    -.2550612    .3940836
           4 1  |   .0492307   .1647633     0.30   0.765     -.273853    .3723144
                |
          _cons |   3.210191   .1040577    30.85   0.000     3.006145    3.414238
---------------------------------------------------------------------------------

.         margins r.panel, at(attention=1) post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()
at           : attention       =           1

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        9.27     0.0023
   (3 vs 1)  |          1        3.52     0.0607
   (4 vs 1)  |          1       24.43     0.0000
      Joint  |          3        8.68     0.0000
             |
 Denominator |       2546
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |   .2608932   .0856702      .0929028    .4288835
   (3 vs 1)  |   .1619517   .0863039     -.0072812    .3311846
   (4 vs 1)  |   .4169617   .0843584      .2515436    .5823797
--------------------------------------------------------------

.         estimates store trust1  

. reg american i.panel##i.attention, robust

Linear regression                               Number of obs     =      2,557
                                                F(7, 2549)        =       2.07
                                                Prob > F          =     0.0432
                                                R-squared         =     0.0053
                                                Root MSE          =     1.4023

---------------------------------------------------------------------------------
                |               Robust
       american |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
          panel |
             2  |  -.0391932   .1563293    -0.25   0.802    -.3457386    .2673522
             3  |  -.0863827   .1551451    -0.56   0.578     -.390606    .2178405
             4  |   .2702692   .1472211     1.84   0.067     -.018416    .5589543
                |
    1.attention |  -.0376124   .1270476    -0.30   0.767    -.2867395    .2115146
                |
panel#attention |
           2 1  |   .1581088   .1819991     0.87   0.385    -.1987723    .5149898
           3 1  |   .1315515   .1803445     0.73   0.466    -.2220852    .4851881
           4 1  |  -.0391036   .1739387    -0.22   0.822    -.3801791    .3019719
                |
          _cons |    3.44586   .1076572    32.01   0.000     3.234755    3.656964
---------------------------------------------------------------------------------

.         margins r.panel, at(attention=0) post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()
at           : attention       =           0

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        0.06     0.8021
   (3 vs 1)  |          1        0.31     0.5777
   (4 vs 1)  |          1        3.37     0.0665
      Joint  |          3        2.38     0.0679
             |
 Denominator |       2549
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |  -.0391932   .1563293     -.3457386    .2673522
   (3 vs 1)  |  -.0863827   .1551451      -.390606    .2178405
   (4 vs 1)  |   .2702692   .1472211      -.018416    .5589543
--------------------------------------------------------------

.         estimates store american0

. reg american i.panel##i.attention, robust

Linear regression                               Number of obs     =      2,557
                                                F(7, 2549)        =       2.07
                                                Prob > F          =     0.0432
                                                R-squared         =     0.0053
                                                Root MSE          =     1.4023

---------------------------------------------------------------------------------
                |               Robust
       american |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
          panel |
             2  |  -.0391932   .1563293    -0.25   0.802    -.3457386    .2673522
             3  |  -.0863827   .1551451    -0.56   0.578     -.390606    .2178405
             4  |   .2702692   .1472211     1.84   0.067     -.018416    .5589543
                |
    1.attention |  -.0376124   .1270476    -0.30   0.767    -.2867395    .2115146
                |
panel#attention |
           2 1  |   .1581088   .1819991     0.87   0.385    -.1987723    .5149898
           3 1  |   .1315515   .1803445     0.73   0.466    -.2220852    .4851881
           4 1  |  -.0391036   .1739387    -0.22   0.822    -.3801791    .3019719
                |
          _cons |    3.44586   .1076572    32.01   0.000     3.234755    3.656964
---------------------------------------------------------------------------------

.         margins r.panel, at(attention=1) post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()
at           : attention       =           1

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        1.63     0.2021
   (3 vs 1)  |          1        0.24     0.6233
   (4 vs 1)  |          1        6.23     0.0126
      Joint  |          3        2.44     0.0626
             |
 Denominator |       2549
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |   .1189156   .0931923     -.0638247    .3016558
   (3 vs 1)  |   .0451687   .0919464     -.1351284    .2254659
   (4 vs 1)  |   .2311656   .0926316      .0495247    .4128065
--------------------------------------------------------------

.         estimates store american1       

. reg refugee i.panel##i.attention, robust

Linear regression                               Number of obs     =      2,557
                                                F(7, 2549)        =       2.13
                                                Prob > F          =     0.0373
                                                R-squared         =     0.0055
                                                Root MSE          =     1.3874

---------------------------------------------------------------------------------
                |               Robust
        refugee |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
          panel |
             2  |  -.1028875   .1549303    -0.66   0.507    -.4066895    .2009146
             3  |  -.2023646   .1528713    -1.32   0.186    -.5021291    .0973999
             4  |  -.0127799   .1559825    -0.08   0.935    -.3186453    .2930854
                |
    1.attention |  -.0909974   .1275202    -0.71   0.476    -.3410511    .1590562
                |
panel#attention |
           2 1  |   .2376507   .1802821     1.32   0.188    -.1158636     .591165
           3 1  |   .2165201   .1784848     1.21   0.225    -.1334698      .56651
           4 1  |   .2608899   .1803019     1.45   0.148    -.0926631    .6144429
                |
          _cons |   3.509554   .1081185    32.46   0.000     3.297545    3.721563
---------------------------------------------------------------------------------

.         margins r.panel, at(attention=0) post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()
at           : attention       =           0

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        0.44     0.5067
   (3 vs 1)  |          1        1.75     0.1857
   (4 vs 1)  |          1        0.01     0.9347
      Joint  |          3        0.74     0.5263
             |
 Denominator |       2549
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |  -.1028875   .1549303     -.4066895    .2009146
   (3 vs 1)  |  -.2023646   .1528713     -.5021291    .0973999
   (4 vs 1)  |  -.0127799   .1559825     -.3186453    .2930854
--------------------------------------------------------------

.         estimates store refugee0          

. reg refugee i.panel##i.attention, robust

Linear regression                               Number of obs     =      2,557
                                                F(7, 2549)        =       2.13
                                                Prob > F          =     0.0373
                                                R-squared         =     0.0055
                                                Root MSE          =     1.3874

---------------------------------------------------------------------------------
                |               Robust
        refugee |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
          panel |
             2  |  -.1028875   .1549303    -0.66   0.507    -.4066895    .2009146
             3  |  -.2023646   .1528713    -1.32   0.186    -.5021291    .0973999
             4  |  -.0127799   .1559825    -0.08   0.935    -.3186453    .2930854
                |
    1.attention |  -.0909974   .1275202    -0.71   0.476    -.3410511    .1590562
                |
panel#attention |
           2 1  |   .2376507   .1802821     1.32   0.188    -.1158636     .591165
           3 1  |   .2165201   .1784848     1.21   0.225    -.1334698      .56651
           4 1  |   .2608899   .1803019     1.45   0.148    -.0926631    .6144429
                |
          _cons |   3.509554   .1081185    32.46   0.000     3.297545    3.721563
---------------------------------------------------------------------------------

.         margins r.panel, at(attention=1) post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()
at           : attention       =           1

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        2.14     0.1439
   (3 vs 1)  |          1        0.02     0.8779
   (4 vs 1)  |          1        7.53     0.0061
      Joint  |          3        3.47     0.0154
             |
 Denominator |       2549
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |   .1347632   .0921859     -.0460037    .3155301
   (3 vs 1)  |   .0141555   .0921259     -.1664938    .1948048
   (4 vs 1)  |     .24811   .0904335      .0707794    .4254405
--------------------------------------------------------------

.         estimates store refugee1        

.         
. coefplot fair0 fair1, bylabel("UNHCR is Fair") || ///
>                  trust0 trust1, bylabel("UNHCR can be Trusted") || /// 
>                  american0 american1, bylabel("Outcome Good for Americans") || ///
>                  refugee0 refugee1, bylabel("Outcome Good for Refugees") format(%9.2g) ///
>                  vertical scheme(plottig) ///
>                  yline(0) ciopts(recast(rcap)) ///
>                  xlabel(1 `""Mixed-Race" "All-Male""' 2 `""All-White" "Mixed-Gender""' ///
>                             3 `""Mixed-Race" "Mixed-Gender""') ///
>                  xtitle("Racial and Gender Distribution of UNHCR Panel") ///
>                  ytitle("Marginal Treatment Effect" "(All-White vs. Diverse Panels") ///
>                  legend(order(2 "Low Attention" 4 "High Attention") rows(1))
(note:  clockdir by_legend_position not found in scheme, default attributes used)

.                  
. *** Figure A7: UNHCR Diversity and Procedural Legitimacy Replication Comparison
. use "CH_main.dta", clear // 2019 Main Survey Sample

. 
. reg american i.panel if outcome==1, robust

Linear regression                               Number of obs     =      1,285
                                                F(3, 1281)        =       3.74
                                                Prob > F          =     0.0108
                                                R-squared         =     0.0085
                                                Root MSE          =     1.4394

------------------------------------------------------------------------------
             |               Robust
    american |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |   .2051015    .116797     1.76   0.079    -.0240328    .4342358
          3  |   .1408613   .1146841     1.23   0.220    -.0841279    .3658505
          4  |   .3708055   .1130001     3.28   0.001     .1491199    .5924911
             |
       _cons |    3.17378   .0831266    38.18   0.000     3.010701     3.33686
------------------------------------------------------------------------------

.         margins r.panel, vsquish post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        3.08     0.0793
   (3 vs 1)  |          1        1.51     0.2196
   (4 vs 1)  |          1       10.77     0.0011
      Joint  |          3        3.74     0.0108
             |
 Denominator |       1281
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |   .2051015    .116797     -.0240328    .4342358
   (3 vs 1)  |   .1408613   .1146841     -.0841279    .3658505
   (4 vs 1)  |   .3708055   .1130001      .1491199    .5924911
--------------------------------------------------------------

.         estimates store american1

. reg american i.panel if outcome==2, robust

Linear regression                               Number of obs     =      1,272
                                                F(3, 1268)        =       1.55
                                                Prob > F          =     0.2010
                                                R-squared         =     0.0036
                                                Root MSE          =     1.3457

------------------------------------------------------------------------------
             |               Robust
    american |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |   -.053513   .1074043    -0.50   0.618    -.2642227    .1571967
          3  |  -.1227682   .1066399    -1.15   0.250    -.3319782    .0864419
          4  |   .0984657    .107184     0.92   0.358    -.1118118    .3087433
             |
       _cons |   3.671975   .0762348    48.17   0.000     3.522414    3.821535
------------------------------------------------------------------------------

.         margins r.panel, vsquish post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        0.25     0.6184
   (3 vs 1)  |          1        1.33     0.2499
   (4 vs 1)  |          1        0.84     0.3584
      Joint  |          3        1.55     0.2010
             |
 Denominator |       1268
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |   -.053513   .1074043     -.2642227    .1571967
   (3 vs 1)  |  -.1227682   .1066399     -.3319782    .0864419
   (4 vs 1)  |   .0984657    .107184     -.1118118    .3087433
--------------------------------------------------------------

.         estimates store american2       

. reg refugee i.panel if outcome==1, robust

Linear regression                               Number of obs     =      1,285
                                                F(3, 1281)        =       2.57
                                                Prob > F          =     0.0528
                                                R-squared         =     0.0061
                                                Root MSE          =     1.4418

------------------------------------------------------------------------------
             |               Robust
     refugee |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |   .2279579   .1169683     1.95   0.052    -.0015125    .4574283
          3  |   .0793443   .1144189     0.69   0.488    -.1451247    .3038133
          4  |   .2800606   .1145913     2.44   0.015     .0552533    .5048679
             |
       _cons |   3.067073   .0838764    36.57   0.000     2.902523    3.231623
------------------------------------------------------------------------------

.         margins r.panel, vsquish post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        3.80     0.0515
   (3 vs 1)  |          1        0.48     0.4882
   (4 vs 1)  |          1        5.97     0.0147
      Joint  |          3        2.57     0.0528
             |
 Denominator |       1281
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |   .2279579   .1169683     -.0015125    .4574283
   (3 vs 1)  |   .0793443   .1144189     -.1451247    .3038133
   (4 vs 1)  |   .2800606   .1145913      .0552533    .5048679
--------------------------------------------------------------

.         estimates store refugee1

. reg refugee i.panel if outcome==2, robust

Linear regression                               Number of obs     =      1,272
                                                F(3, 1268)        =       2.12
                                                Prob > F          =     0.0960
                                                R-squared         =     0.0049
                                                Root MSE          =      1.267

------------------------------------------------------------------------------
             |               Robust
     refugee |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |  -.0896717   .1010355    -0.89   0.375    -.2878869    .1085435
          3  |  -.1677181   .1023246    -1.64   0.101    -.3684623     .033026
          4  |   .0681609   .0996971     0.68   0.494    -.1274286    .2637504
             |
       _cons |    3.83121   .0721792    53.08   0.000     3.689606    3.972814
------------------------------------------------------------------------------

.         margins r.panel, vsquish post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        0.79     0.3750
   (3 vs 1)  |          1        2.69     0.1014
   (4 vs 1)  |          1        0.47     0.4943
      Joint  |          3        2.12     0.0960
             |
 Denominator |       1268
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |  -.0896717   .1010355     -.2878869    .1085435
   (3 vs 1)  |  -.1677181   .1023246     -.3684623     .033026
   (4 vs 1)  |   .0681609   .0996971     -.1274286    .2637504
--------------------------------------------------------------

.         estimates store refugee2        

. reg fair i.panel if outcome==1, robust

Linear regression                               Number of obs     =      1,284
                                                F(3, 1280)        =      13.17
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0279
                                                Root MSE          =     1.2999

------------------------------------------------------------------------------
             |               Robust
        fair |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |   .2148917   .1053244     2.04   0.042     .0082643     .421519
          3  |   .1185213   .1036451     1.14   0.253    -.0848116    .3218543
          4  |   .5895604   .1009007     5.84   0.000     .3916114    .7875093
             |
       _cons |   2.865854   .0741764    38.64   0.000     2.720333    3.011374
------------------------------------------------------------------------------

.         margins r.panel, vsquish post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        4.16     0.0415
   (3 vs 1)  |          1        1.31     0.2530
   (4 vs 1)  |          1       34.14     0.0000
      Joint  |          3       13.17     0.0000
             |
 Denominator |       1280
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |   .2148917   .1053244      .0082643     .421519
   (3 vs 1)  |   .1185213   .1036451     -.0848116    .3218543
   (4 vs 1)  |   .5895604   .1009007      .3916114    .7875093
--------------------------------------------------------------

.         estimates store fair1

. reg fair i.panel if outcome==2, robust

Linear regression                               Number of obs     =      1,272
                                                F(3, 1268)        =       2.64
                                                Prob > F          =     0.0480
                                                R-squared         =     0.0063
                                                Root MSE          =     1.1677

------------------------------------------------------------------------------
             |               Robust
        fair |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |   .0414699   .0927825     0.45   0.655    -.1405543    .2234941
          3  |    .151653   .0926204     1.64   0.102    -.0300531    .3333591
          4  |   .2361094   .0935213     2.52   0.012     .0526359     .419583
             |
       _cons |   3.229299   .0662582    48.74   0.000     3.099312    3.359287
------------------------------------------------------------------------------

.         margins r.panel, vsquish post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        0.20     0.6550
   (3 vs 1)  |          1        2.68     0.1018
   (4 vs 1)  |          1        6.37     0.0117
      Joint  |          3        2.64     0.0480
             |
 Denominator |       1268
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |   .0414699   .0927825     -.1405543    .2234941
   (3 vs 1)  |    .151653   .0926204     -.0300531    .3333591
   (4 vs 1)  |   .2361094   .0935213      .0526359     .419583
--------------------------------------------------------------

.         estimates store fair2   

. reg trust i.panel if outcome==2, robust

Linear regression                               Number of obs     =      1,270
                                                F(3, 1266)        =       3.48
                                                Prob > F          =     0.0154
                                                R-squared         =     0.0082
                                                Root MSE          =     1.1997

------------------------------------------------------------------------------
             |               Robust
       trust |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |   .0566958    .096328     0.59   0.556    -.1322843    .2456758
          3  |   .1258316   .0971957     1.29   0.196    -.0648508     .316514
          4  |   .2899395   .0965493     3.00   0.003     .1005254    .4793537
             |
       _cons |   3.363057   .0703634    47.80   0.000     3.225016    3.501099
------------------------------------------------------------------------------

.         margins r.panel, vsquish post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        0.35     0.5563
   (3 vs 1)  |          1        1.68     0.1957
   (4 vs 1)  |          1        9.02     0.0027
      Joint  |          3        3.48     0.0154
             |
 Denominator |       1266
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |   .0566958    .096328     -.1322843    .2456758
   (3 vs 1)  |   .1258316   .0971957     -.0648508     .316514
   (4 vs 1)  |   .2899395   .0965493      .1005254    .4793537
--------------------------------------------------------------

.         estimates store trust1  

. reg trust i.panel if outcome==2, robust

Linear regression                               Number of obs     =      1,270
                                                F(3, 1266)        =       3.48
                                                Prob > F          =     0.0154
                                                R-squared         =     0.0082
                                                Root MSE          =     1.1997

------------------------------------------------------------------------------
             |               Robust
       trust |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |   .0566958    .096328     0.59   0.556    -.1322843    .2456758
          3  |   .1258316   .0971957     1.29   0.196    -.0648508     .316514
          4  |   .2899395   .0965493     3.00   0.003     .1005254    .4793537
             |
       _cons |   3.363057   .0703634    47.80   0.000     3.225016    3.501099
------------------------------------------------------------------------------

.         margins r.panel, vsquish post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        0.35     0.5563
   (3 vs 1)  |          1        1.68     0.1957
   (4 vs 1)  |          1        9.02     0.0027
      Joint  |          3        3.48     0.0154
             |
 Denominator |       1266
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |   .0566958    .096328     -.1322843    .2456758
   (3 vs 1)  |   .1258316   .0971957     -.0648508     .316514
   (4 vs 1)  |   .2899395   .0965493      .1005254    .4793537
--------------------------------------------------------------

.         estimates store trust2          

.         
. use "CH_followup.dta", clear // 2021 Sample w/o country labels

.         keep if country==1      
(1,478 observations deleted)

. 
. reg american i.panel if outcome==1, robust

Linear regression                               Number of obs     =        710
                                                F(3, 706)         =       0.90
                                                Prob > F          =     0.4400
                                                R-squared         =     0.0038
                                                Root MSE          =     1.2249

------------------------------------------------------------------------------
             |               Robust
    american |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |   .1688288   .1336097     1.26   0.207    -.0934911    .4311488
          3  |    .020925   .1302971     0.16   0.872    -.2348911    .2767411
          4  |   .1533224   .1280985     1.20   0.232    -.0981771    .4048219
             |
       _cons |   3.437838   .0935027    36.77   0.000     3.254261    3.621415
------------------------------------------------------------------------------

.         margins r.panel, vsquish post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        1.60     0.2068
   (3 vs 1)  |          1        0.03     0.8725
   (4 vs 1)  |          1        1.43     0.2317
      Joint  |          3        0.90     0.4400
             |
 Denominator |        706
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |   .1688288   .1336097     -.0934911    .4311488
   (3 vs 1)  |    .020925   .1302971     -.2348911    .2767411
   (4 vs 1)  |   .1533224   .1280985     -.0981771    .4048219
--------------------------------------------------------------

.         estimates store american3

. reg american i.panel if outcome==2, robust

Linear regression                               Number of obs     =        718
                                                F(3, 714)         =       0.92
                                                Prob > F          =     0.4291
                                                R-squared         =     0.0039
                                                Root MSE          =     1.2141

------------------------------------------------------------------------------
             |               Robust
    american |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |  -.0841014    .130883    -0.64   0.521    -.3410629    .1728601
          3  |   .0981655   .1301494     0.75   0.451    -.1573558    .3536868
          4  |     .09412   .1277274     0.74   0.461    -.1566462    .3448861
             |
       _cons |   3.535714   .0935798    37.78   0.000      3.35199    3.719439
------------------------------------------------------------------------------

.         margins r.panel, vsquish post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        0.41     0.5207
   (3 vs 1)  |          1        0.57     0.4509
   (4 vs 1)  |          1        0.54     0.4614
      Joint  |          3        0.92     0.4291
             |
 Denominator |        714
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |  -.0841014    .130883     -.3410629    .1728601
   (3 vs 1)  |   .0981655   .1301494     -.1573558    .3536868
   (4 vs 1)  |     .09412   .1277274     -.1566462    .3448861
--------------------------------------------------------------

.         estimates store american4       

. reg refugee i.panel if outcome==1, robust

Linear regression                               Number of obs     =        710
                                                F(3, 706)         =       1.99
                                                Prob > F          =     0.1136
                                                R-squared         =     0.0088
                                                Root MSE          =     1.2683

------------------------------------------------------------------------------
             |               Robust
     refugee |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |   .2859459    .140895     2.03   0.043     .0093225    .5625693
          3  |   .1675954   .1364674     1.23   0.220    -.1003351     .435526
          4  |   .2940122   .1334241     2.20   0.028     .0320568    .5559677
             |
       _cons |   3.054054    .100167    30.49   0.000     2.857393    3.250715
------------------------------------------------------------------------------

.         margins r.panel, vsquish post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        4.12     0.0428
   (3 vs 1)  |          1        1.51     0.2198
   (4 vs 1)  |          1        4.86     0.0279
      Joint  |          3        1.99     0.1136
             |
 Denominator |        706
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |   .2859459    .140895      .0093225    .5625693
   (3 vs 1)  |   .1675954   .1364674     -.1003351     .435526
   (4 vs 1)  |   .2940122   .1334241      .0320568    .5559677
--------------------------------------------------------------

.         estimates store refugee3

. reg refugee i.panel if outcome==2, robust

Linear regression                               Number of obs     =        718
                                                F(3, 714)         =       0.94
                                                Prob > F          =     0.4188
                                                R-squared         =     0.0038
                                                Root MSE          =     1.1713

------------------------------------------------------------------------------
             |               Robust
     refugee |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |  -.0716206   .1277095    -0.56   0.575    -.3223516    .1791104
          3  |   .0586456   .1278565     0.46   0.647    -.1923741    .3096653
          4  |    .122106   .1235699     0.99   0.323    -.1204979    .3647099
             |
       _cons |   3.684524     .09291    39.66   0.000     3.502114    3.866933
------------------------------------------------------------------------------

.         margins r.panel, vsquish post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        0.31     0.5751
   (3 vs 1)  |          1        0.21     0.6466
   (4 vs 1)  |          1        0.98     0.3234
      Joint  |          3        0.94     0.4188
             |
 Denominator |        714
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |  -.0716206   .1277095     -.3223516    .1791104
   (3 vs 1)  |   .0586456   .1278565     -.1923741    .3096653
   (4 vs 1)  |    .122106   .1235699     -.1204979    .3647099
--------------------------------------------------------------

.         estimates store refugee4        

. reg fair i.panel if outcome==1, robust

Linear regression                               Number of obs     =        710
                                                F(3, 706)         =      10.25
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0369
                                                Root MSE          =     1.1611

------------------------------------------------------------------------------
             |               Robust
        fair |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |   .2915315   .1300614     2.24   0.025     .0361781     .546885
          3  |   .2153803    .128323     1.68   0.094    -.0365601    .4673207
          4  |   .6217709   .1171008     5.31   0.000     .3918634    .8516785
             |
       _cons |   3.135135   .0916116    34.22   0.000     2.955271    3.314999
------------------------------------------------------------------------------

.         margins r.panel, vsquish post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        5.02     0.0253
   (3 vs 1)  |          1        2.82     0.0937
   (4 vs 1)  |          1       28.19     0.0000
      Joint  |          3       10.25     0.0000
             |
 Denominator |        706
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |   .2915315   .1300614      .0361781     .546885
   (3 vs 1)  |   .2153803    .128323     -.0365601    .4673207
   (4 vs 1)  |   .6217709   .1171008      .3918634    .8516785
--------------------------------------------------------------

.         estimates store fair3

. reg fair i.panel if outcome==2, robust

Linear regression                               Number of obs     =        718
                                                F(3, 714)         =       3.03
                                                Prob > F          =     0.0287
                                                R-squared         =     0.0123
                                                Root MSE          =     1.0381

------------------------------------------------------------------------------
             |               Robust
        fair |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |     .09351   .1156222     0.81   0.419    -.1334902    .3205102
          3  |   .2167252   .1125607     1.93   0.055    -.0042643    .4377147
          4  |   .3066627   .1100273     2.79   0.005     .0906469    .5226785
             |
       _cons |   3.422619   .0837781    40.85   0.000     3.258138      3.5871
------------------------------------------------------------------------------

.         margins r.panel, vsquish post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        0.65     0.4189
   (3 vs 1)  |          1        3.71     0.0546
   (4 vs 1)  |          1        7.77     0.0055
      Joint  |          3        3.03     0.0287
             |
 Denominator |        714
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |     .09351   .1156222     -.1334902    .3205102
   (3 vs 1)  |   .2167252   .1125607     -.0042643    .4377147
   (4 vs 1)  |   .3066627   .1100273      .0906469    .5226785
--------------------------------------------------------------

.         estimates store fair4   

. reg trust i.panel if outcome==1, robust

Linear regression                               Number of obs     =        710
                                                F(3, 706)         =       3.70
                                                Prob > F          =     0.0117
                                                R-squared         =     0.0146
                                                Root MSE          =     1.2358

------------------------------------------------------------------------------
             |               Robust
       trust |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |   .2118919   .1390005     1.52   0.128    -.0610119    .4847957
          3  |   .1238507    .132586     0.93   0.351    -.1364593    .3841606
          4  |    .405704   .1274967     3.18   0.002      .155386    .6560221
             |
       _cons |   3.108108   .0963357    32.26   0.000     2.918969    3.297247
------------------------------------------------------------------------------

.         margins r.panel, vsquish post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        2.32     0.1279
   (3 vs 1)  |          1        0.87     0.3506
   (4 vs 1)  |          1       10.13     0.0015
      Joint  |          3        3.70     0.0117
             |
 Denominator |        706
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |   .2118919   .1390005     -.0610119    .4847957
   (3 vs 1)  |   .1238507    .132586     -.1364593    .3841606
   (4 vs 1)  |    .405704   .1274967       .155386    .6560221
--------------------------------------------------------------

.         estimates store trust3  

. reg trust i.panel if outcome==2, robust

Linear regression                               Number of obs     =        718
                                                F(3, 714)         =       3.76
                                                Prob > F          =     0.0108
                                                R-squared         =     0.0148
                                                Root MSE          =     1.0761

------------------------------------------------------------------------------
             |               Robust
       trust |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |   .1443932   .1167377     1.24   0.217     -.084797    .3735834
          3  |   .1131928   .1138502     0.99   0.320    -.1103285    .3367141
          4  |   .3662194   .1129934     3.24   0.001     .1443804    .5880584
             |
       _cons |   3.285714   .0823393    39.90   0.000     3.124058     3.44737
------------------------------------------------------------------------------

.         margins r.panel, vsquish post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        1.53     0.2165
   (3 vs 1)  |          1        0.99     0.3204
   (4 vs 1)  |          1       10.50     0.0012
      Joint  |          3        3.76     0.0108
             |
 Denominator |        714
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |   .1443932   .1167377      -.084797    .3735834
   (3 vs 1)  |   .1131928   .1138502     -.1103285    .3367141
   (4 vs 1)  |   .3662194   .1129934      .1443804    .5880584
--------------------------------------------------------------

.         estimates store trust4  

.         
. use "CH_followup.dta", clear // 2021 Sample w/ country labels

.         keep if country==2
(1,428 observations deleted)

. 
. reg american i.panel if outcome==1, robust

Linear regression                               Number of obs     =        772
                                                F(3, 768)         =       0.31
                                                Prob > F          =     0.8166
                                                R-squared         =     0.0012
                                                Root MSE          =     1.2455

------------------------------------------------------------------------------
             |               Robust
    american |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |  -.0371409    .127103    -0.29   0.770    -.2866513    .2123695
          3  |   .0276027   .1254985     0.22   0.826     -.218758    .2739635
          4  |   .0822503   .1256256     0.65   0.513    -.1643601    .3288607
             |
       _cons |   3.497537   .0884534    39.54   0.000     3.323898    3.671176
------------------------------------------------------------------------------

.         margins r.panel, vsquish post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        0.09     0.7702
   (3 vs 1)  |          1        0.05     0.8260
   (4 vs 1)  |          1        0.43     0.5128
      Joint  |          3        0.31     0.8166
             |
 Denominator |        768
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |  -.0371409    .127103     -.2866513    .2123695
   (3 vs 1)  |   .0276027   .1254985      -.218758    .2739635
   (4 vs 1)  |   .0822503   .1256256     -.1643601    .3288607
--------------------------------------------------------------

.         estimates store american5

. reg american i.panel if outcome==2, robust

Linear regression                               Number of obs     =        706
                                                F(3, 702)         =       0.33
                                                Prob > F          =     0.8029
                                                R-squared         =     0.0014
                                                Root MSE          =     1.2998

------------------------------------------------------------------------------
             |               Robust
    american |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |    .023385   .1358543     0.17   0.863    -.2433444    .2901144
          3  |   .0934264   .1348584     0.69   0.489    -.1713478    .3582006
          4  |   .1191547   .1391988     0.86   0.392    -.1541411    .3924506
             |
       _cons |   3.377778   .0939417    35.96   0.000     3.193338    3.562218
------------------------------------------------------------------------------

.         margins r.panel, vsquish post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        0.03     0.8634
   (3 vs 1)  |          1        0.48     0.4887
   (4 vs 1)  |          1        0.73     0.3923
      Joint  |          3        0.33     0.8029
             |
 Denominator |        702
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |    .023385   .1358543     -.2433444    .2901144
   (3 vs 1)  |   .0934264   .1348584     -.1713478    .3582006
   (4 vs 1)  |   .1191547   .1391988     -.1541411    .3924506
--------------------------------------------------------------

.         estimates store american6       

. reg refugee i.panel if outcome==1, robust

Linear regression                               Number of obs     =        772
                                                F(3, 768)         =       1.94
                                                Prob > F          =     0.1213
                                                R-squared         =     0.0075
                                                Root MSE          =     1.2671

------------------------------------------------------------------------------
             |               Robust
     refugee |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |   .0799151   .1302336     0.61   0.540     -.175741    .3355712
          3  |   .2258579   .1280551     1.76   0.078    -.0255216    .4772375
          4  |   .2720365   .1279898     2.13   0.034     .0207852    .5232878
             |
       _cons |   3.142857   .0911839    34.47   0.000     2.963858    3.321856
------------------------------------------------------------------------------

.         margins r.panel, vsquish post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        0.38     0.5396
   (3 vs 1)  |          1        3.11     0.0782
   (4 vs 1)  |          1        4.52     0.0339
      Joint  |          3        1.94     0.1213
             |
 Denominator |        768
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |   .0799151   .1302336      -.175741    .3355712
   (3 vs 1)  |   .2258579   .1280551     -.0255216    .4772375
   (4 vs 1)  |   .2720365   .1279898      .0207852    .5232878
--------------------------------------------------------------

.         estimates store refugee5

. reg refugee i.panel if outcome==2, robust

Linear regression                               Number of obs     =        706
                                                F(3, 702)         =       0.75
                                                Prob > F          =     0.5204
                                                R-squared         =     0.0032
                                                Root MSE          =     1.2669

------------------------------------------------------------------------------
             |               Robust
     refugee |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |   .1249354    .137294     0.91   0.363    -.1446207    .3944915
          3  |   .0566899   .1296237     0.44   0.662    -.1978067    .3111865
          4  |   .1934901   .1368802     1.41   0.158    -.0752536    .4622338
             |
       _cons |   3.561111   .0942742    37.77   0.000     3.376018    3.746204
------------------------------------------------------------------------------

.         margins r.panel, vsquish post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        0.83     0.3631
   (3 vs 1)  |          1        0.19     0.6620
   (4 vs 1)  |          1        2.00     0.1579
      Joint  |          3        0.75     0.5204
             |
 Denominator |        702
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |   .1249354    .137294     -.1446207    .3944915
   (3 vs 1)  |   .0566899   .1296237     -.1978067    .3111865
   (4 vs 1)  |   .1934901   .1368802     -.0752536    .4622338
--------------------------------------------------------------

.         estimates store refugee6        

. reg fair i.panel if outcome==1, robust

Linear regression                               Number of obs     =        772
                                                F(3, 768)         =       7.71
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0267
                                                Root MSE          =      1.139

------------------------------------------------------------------------------
             |               Robust
        fair |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |   .1447349   .1169634     1.24   0.216     -.084871    .3743408
          3  |   .2495803   .1232289     2.03   0.043     .0076749    .4914857
          4  |   .5135468    .112982     4.55   0.000     .2917566     .735337
             |
       _cons |   3.236453   .0859216    37.67   0.000     3.067784    3.405122
------------------------------------------------------------------------------

.         margins r.panel, vsquish post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        1.53     0.2163
   (3 vs 1)  |          1        4.10     0.0432
   (4 vs 1)  |          1       20.66     0.0000
      Joint  |          3        7.71     0.0000
             |
 Denominator |        768
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |   .1447349   .1169634      -.084871    .3743408
   (3 vs 1)  |   .2495803   .1232289      .0076749    .4914857
   (4 vs 1)  |   .5135468    .112982      .2917566     .735337
--------------------------------------------------------------

.         estimates store fair5

. reg fair i.panel if outcome==2, robust

Linear regression                               Number of obs     =        706
                                                F(3, 702)         =       2.37
                                                Prob > F          =     0.0692
                                                R-squared         =     0.0099
                                                Root MSE          =     1.1269

------------------------------------------------------------------------------
             |               Robust
        fair |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |   .1624031   .1192151     1.36   0.174    -.0716577    .3964639
          3  |   .1254799   .1155667     1.09   0.278    -.1014179    .3523778
          4  |   .3204499   .1209332     2.65   0.008     .0830159    .5578839
             |
       _cons |   3.366667   .0814639    41.33   0.000     3.206725    3.526609
------------------------------------------------------------------------------

.         margins r.panel, vsquish post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        1.86     0.1735
   (3 vs 1)  |          1        1.18     0.2779
   (4 vs 1)  |          1        7.02     0.0082
      Joint  |          3        2.37     0.0692
             |
 Denominator |        702
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |   .1624031   .1192151     -.0716577    .3964639
   (3 vs 1)  |   .1254799   .1155667     -.1014179    .3523778
   (4 vs 1)  |   .3204499   .1209332      .0830159    .5578839
--------------------------------------------------------------

.         estimates store fair6   

. reg trust i.panel if outcome==1, robust

Linear regression                               Number of obs     =        772
                                                F(3, 768)         =       3.00
                                                Prob > F          =     0.0298
                                                R-squared         =     0.0114
                                                Root MSE          =      1.241

------------------------------------------------------------------------------
             |               Robust
       trust |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |   .1296396   .1270563     1.02   0.308    -.1197793    .3790585
          3  |   .2318023   .1286446     1.80   0.072    -.0207345    .4843391
          4  |   .3606802    .124786     2.89   0.004     .1157181    .6056423
             |
       _cons |   3.187192   .0906513    35.16   0.000     3.009238    3.365146
------------------------------------------------------------------------------

.         margins r.panel, vsquish post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        1.04     0.3079
   (3 vs 1)  |          1        3.25     0.0720
   (4 vs 1)  |          1        8.35     0.0040
      Joint  |          3        3.00     0.0298
             |
 Denominator |        768
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |   .1296396   .1270563     -.1197793    .3790585
   (3 vs 1)  |   .2318023   .1286446     -.0207345    .4843391
   (4 vs 1)  |   .3606802    .124786      .1157181    .6056423
--------------------------------------------------------------

.         estimates store trust5  

. reg trust i.panel if outcome==2, robust

Linear regression                               Number of obs     =        706
                                                F(3, 702)         =       2.14
                                                Prob > F          =     0.0940
                                                R-squared         =     0.0089
                                                Root MSE          =     1.2246

------------------------------------------------------------------------------
             |               Robust
       trust |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |   .1165375   .1297923     0.90   0.370    -.1382902    .3713651
          3  |    .079523   .1239228     0.64   0.521    -.1637808    .3228268
          4  |   .3211316   .1300476     2.47   0.014     .0658027    .5764604
             |
       _cons |   3.255556   .0863551    37.70   0.000      3.08601    3.425101
------------------------------------------------------------------------------

.         margins r.panel, vsquish post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        0.81     0.3696
   (3 vs 1)  |          1        0.41     0.5213
   (4 vs 1)  |          1        6.10     0.0138
      Joint  |          3        2.14     0.0940
             |
 Denominator |        702
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |   .1165375   .1297923     -.1382902    .3713651
   (3 vs 1)  |    .079523   .1239228     -.1637808    .3228268
   (4 vs 1)  |   .3211316   .1300476      .0658027    .5764604
--------------------------------------------------------------

.         estimates store trust6          

.         
. coefplot (fair1, color(538g) ciopts(lcolor(538g) recast(rcap))) ///
>                  (fair3, color(538b) ciopts(lcolor(538b) recast(rcap))) ///
>                  (fair5, color(538r) ciopts(lcolor(538r) recast(rcap))), ///
>                   bylabel("(a) UNHCR is Fair (Non-critical Report)") || ///
>                  (fair2, color(538g) ciopts(lcolor(538g) recast(rcap))) ///
>                  (fair4, color(538b) ciopts(lcolor(538b) recast(rcap))) ///
>                  (fair6, color(538r) ciopts(lcolor(538r) recast(rcap))), ///
>                   bylabel("(b) UNHCR is Fair (Critical Report)") || ///
>                  (trust1, color(538g) ciopts(lcolor(538g) recast(rcap))) ///
>                  (trust3, color(538b) ciopts(lcolor(538b) recast(rcap))) ///
>                  (trust5, color(538r) ciopts(lcolor(538r) recast(rcap))), ///
>                  bylabel("(c) UNHCR Can Be Trusted (Non-critical Report)") || ///
>                  (trust2, color(538g) ciopts(lcolor(538g) recast(rcap))) ///
>                  (trust4, color(538b) ciopts(lcolor(538b) recast(rcap))) ///
>                  (trust6, color(538r) ciopts(lcolor(538r) recast(rcap))), ///
>                  bylabel("(d) UNHCR Can Be Trusted (Critical Report)")  ///
>                  vertical ciopts(recast(rcap)) ///
>                  ytitle("Marginal Difference (All-White, All-Male Panel vs. Diverse Panels)") ///
>                  scheme(plottig) yline(0) ///
>                  xtitle("Racial and Gender Distribution of Panel (All-White, All-Male as Reference 
> Group)") ///
>                  xlabel(1 `""Mixed-Race" "All-Male""' 2 `""All-White" "Mixed-Gender""' ///
>                                 3 `""Mixed-Race" "Mixed-Gender""', labcol(black)) ///
>                  legend(order(2 "2019 Sample" 4 "2021 Sample (No Country Labels)" ///
>                             6 "2021 Sample (Country Labels)") rows(1))            
(note:  clockdir by_legend_position not found in scheme, default attributes used)

. 
. * Figure A8: UNHCR Diversity and Substantive Legitimacy Replication Comparison                     
>      
.                  
. coefplot (refugee1, color(538g) ciopts(lcolor(538g) recast(rcap))) ///
>                  (refugee3, color(538b) ciopts(lcolor(538b) recast(rcap))) ///
>                  (refugee5, color(538r) ciopts(lcolor(538r) recast(rcap))), ///
>                   bylabel("(a) Outcome Good for Refugees (Non-critical Report)") || ///
>                  (refugee2, color(538g) ciopts(lcolor(538g) recast(rcap))) ///
>                  (refugee4, color(538b) ciopts(lcolor(538b) recast(rcap))) ///
>                  (refugee6, color(538r) ciopts(lcolor(538r) recast(rcap))), ///
>                   bylabel("(b) Outcome Good for Refugees (Critical Report)") || ///
>                  (american1, color(538g) ciopts(lcolor(538g) recast(rcap))) ///
>                  (american3, color(538b) ciopts(lcolor(538b) recast(rcap))) ///
>                  (american5, color(538r) ciopts(lcolor(538r) recast(rcap))), ///
>                  bylabel("(c) Outcome Good for Americans (Non-critical Report)") || ///
>                  (american2, color(538g) ciopts(lcolor(538g) recast(rcap))) ///
>                  (american4, color(538b) ciopts(lcolor(538b) recast(rcap))) ///
>                  (american6, color(538r) ciopts(lcolor(538r) recast(rcap))), ///
>                  bylabel("(d) Outcome Good for Americans (Critical Report)")  ///
>                  vertical ciopts(recast(rcap)) ///
>                  ytitle("Marginal Difference (All-White, All-Male Panel vs. Diverse Panels)") ///
>                  scheme(plottig) yline(0) ///
>                  xtitle("Racial and Gender Distribution of Panel (All-White, All-Male as Reference 
> Group)") ///
>                  xlabel(1 `""Mixed-Race" "All-Male""' 2 `""All-White" "Mixed-Gender""' ///
>                                 3 `""Mixed-Race" "Mixed-Gender""', labcol(black)) ///
>                  legend(order(2 "2019 Sample" 4 "2021 Sample (No Country Labels)" ///
>                             6 "2021 Sample (Country Labels)") rows(1))  
(note:  clockdir by_legend_position not found in scheme, default attributes used)

.           
. ********************************************************************************
. ****************** Appendix A7 *************************************************
. ********************************************************************************  
. 
. *** Figure A9: UNHCR Panel Diversity and Procedural Legitimacy, by Political Attitudes
. use "CH_main.dta", clear        

. // recalculate factor and irt scales
. factor imm1 imm2_1 imm2_2 imm3, pcf
(obs=2,517)

Factor analysis/correlation                      Number of obs    =      2,517
    Method: principal-component factors          Retained factors =          1
    Rotation: (unrotated)                        Number of params =          4

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |      2.59175      1.95401            0.6479       0.6479
        Factor2  |      0.63774      0.11203            0.1594       0.8074
        Factor3  |      0.52571      0.28090            0.1314       0.9388
        Factor4  |      0.24481            .            0.0612       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(6)  = 3892.44 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    ---------------------------------------
        Variable |  Factor1 |   Uniqueness 
    -------------+----------+--------------
            imm1 |   0.7690 |      0.4086  
          imm2_1 |   0.8615 |      0.2578  
          imm2_2 |   0.8669 |      0.2485  
            imm3 |   0.7118 |      0.4934  
    ---------------------------------------

.                 predict immigration
(regression scoring assumed)

Scoring coefficients (method = regression)

    ------------------------
        Variable |  Factor1 
    -------------+----------
            imm1 |  0.29672 
          imm2_1 |  0.33240 
          imm2_2 |  0.33449 
            imm3 |  0.27462 
    ------------------------


. irt grm imm1 imm2_1 imm2_2 imm3

Fitting fixed-effects model:

Iteration 0:   log likelihood = -15301.294  
Iteration 1:   log likelihood = -15301.294  

Fitting full model:

Iteration 0:   log likelihood = -14357.495  
Iteration 1:   log likelihood = -13366.355  
Iteration 2:   log likelihood = -13298.465  
Iteration 3:   log likelihood = -13271.528  
Iteration 4:   log likelihood = -13271.093  
Iteration 5:   log likelihood =  -13271.09  
Iteration 6:   log likelihood =  -13271.09  

Graded response model                           Number of obs     =      2,528
Log likelihood =  -13271.09
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
imm1         |
     Discrim |   1.679896   .0659523    25.47   0.000     1.550632     1.80916
        Diff |
        >=2  |  -1.911726   .0697637   -27.40   0.000    -2.048461   -1.774992
        >=3  |  -.8553684   .0428667   -19.95   0.000    -.9393856   -.7713512
        >=4  |   .5339274   .0378948    14.09   0.000      .459655    .6081999
         =5  |   1.186934   .0507015    23.41   0.000     1.087561    1.286307
-------------+----------------------------------------------------------------
imm2_1       |
     Discrim |   3.596435   .1680964    21.40   0.000     3.266972    3.925898
        Diff |
        >=2  |  -.7326505   .0317797   -23.05   0.000    -.7949375   -.6703635
        >=3  |   .1601078      .0269     5.95   0.000     .1073847    .2128308
        >=4  |   .8224607   .0321508    25.58   0.000     .7594463    .8854751
         =5  |   1.392439   .0426247    32.67   0.000     1.308896    1.475982
-------------+----------------------------------------------------------------
imm2_2       |
     Discrim |   4.337943   .2539303    17.08   0.000     3.840248    4.835637
        Diff |
        >=2  |  -.4912213   .0285159   -17.23   0.000    -.5471114   -.4353311
        >=3  |   .3610225   .0266547    13.54   0.000     .3087802    .4132647
        >=4  |   .9742977    .033451    29.13   0.000     .9087349    1.039861
         =5  |   1.465138   .0432645    33.86   0.000     1.380341    1.549935
-------------+----------------------------------------------------------------
imm3         |
     Discrim |   1.439258   .0592559    24.29   0.000     1.323118    1.555397
        Diff |
        >=2  |  -.9085988   .0491383   -18.49   0.000    -1.004908   -.8122895
        >=3  |   .1571745    .037455     4.20   0.000     .0837641     .230585
        >=4  |   1.007947   .0487844    20.66   0.000     .9123317    1.103563
         =5  |   1.758207   .0711899    24.70   0.000     1.618677    1.897736
------------------------------------------------------------------------------

.                 predict immigration1
(option pr assumed)
(option conditional(ebmeans) assumed)
(using 7 quadrature points)

. factor race1_1 race1_2 race1_3 race1_4 race1_6 race1_8 race2, pcf
(obs=2,509)

Factor analysis/correlation                      Number of obs    =      2,509
    Method: principal-component factors          Retained factors =          1
    Rotation: (unrotated)                        Number of params =          7

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |      4.24615      3.54829            0.6066       0.6066
        Factor2  |      0.69787      0.17229            0.0997       0.7063
        Factor3  |      0.52557      0.03708            0.0751       0.7814
        Factor4  |      0.48850      0.09285            0.0698       0.8512
        Factor5  |      0.39565      0.04606            0.0565       0.9077
        Factor6  |      0.34959      0.05292            0.0499       0.9576
        Factor7  |      0.29667            .            0.0424       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(21) = 8687.12 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    ---------------------------------------
        Variable |  Factor1 |   Uniqueness 
    -------------+----------+--------------
         race1_1 |   0.8615 |      0.2577  
         race1_2 |   0.7166 |      0.4865  
         race1_3 |   0.8144 |      0.3367  
         race1_4 |   0.8067 |      0.3492  
         race1_6 |   0.7190 |      0.4830  
         race1_8 |   0.8090 |      0.3456  
           race2 |   0.7106 |      0.4951  
    ---------------------------------------

.                 predict white_id
(regression scoring assumed)

Scoring coefficients (method = regression)

    ------------------------
        Variable |  Factor1 
    -------------+----------
         race1_1 |  0.20290 
         race1_2 |  0.16875 
         race1_3 |  0.19181 
         race1_4 |  0.18999 
         race1_6 |  0.16933 
         race1_8 |  0.19052 
           race2 |  0.16734 
    ------------------------


. irt grm race1_1 race1_2 race1_3 race1_4 race1_6 race1_8 race2

Fitting fixed-effects model:

Iteration 0:   log likelihood = -26226.749  
Iteration 1:   log likelihood = -26226.749  

Fitting full model:

Iteration 0:   log likelihood = -23636.517  
Iteration 1:   log likelihood = -21673.055  
Iteration 2:   log likelihood = -21603.817  
Iteration 3:   log likelihood = -21599.871  
Iteration 4:   log likelihood = -21599.855  
Iteration 5:   log likelihood = -21599.855  

Graded response model                           Number of obs     =      2,523
Log likelihood = -21599.855
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
race1_1      |
     Discrim |    3.88038    .152367    25.47   0.000     3.581746    4.179014
        Diff |
        >=2  |  -.5060492   .0281229   -17.99   0.000    -.5611689   -.4509294
        >=3  |   .0282052   .0260815     1.08   0.280    -.0229135    .0793239
        >=4  |   .6967418   .0301381    23.12   0.000     .6376723    .7558113
         =5  |    1.72635   .0490032    35.23   0.000     1.630305    1.822395
-------------+----------------------------------------------------------------
race1_2      |
     Discrim |   1.886999   .0676105    27.91   0.000     1.754485    2.019513
        Diff |
        >=2  |  -1.762586   .0620276   -28.42   0.000    -1.884158   -1.641014
        >=3  |  -.8867148     .04076   -21.75   0.000    -.9666028   -.8068267
        >=4  |    .220043   .0330599     6.66   0.000     .1552468    .2848392
         =5  |   1.551798   .0551118    28.16   0.000     1.443781    1.659815
-------------+----------------------------------------------------------------
race1_3      |
     Discrim |   2.801587    .096422    29.06   0.000     2.612604    2.990571
        Diff |
        >=2  |  -.9358541   .0358118   -26.13   0.000    -1.006044   -.8656643
        >=3  |  -.2350935   .0285798    -8.23   0.000    -.2911088   -.1790781
        >=4  |    .554549   .0308349    17.98   0.000     .4941138    .6149842
         =5  |   1.612122   .0493621    32.66   0.000     1.515374     1.70887
-------------+----------------------------------------------------------------
race1_4      |
     Discrim |   2.617925   .0906964    28.86   0.000     2.440164    2.795687
        Diff |
        >=2  |  -.7853555   .0344656   -22.79   0.000    -.8529068   -.7178042
        >=3  |  -.0066236   .0286776    -0.23   0.817    -.0628307    .0495836
        >=4  |   .7888459   .0342219    23.05   0.000     .7217722    .8559197
         =5  |   1.723806   .0533065    32.34   0.000     1.619328    1.828285
-------------+----------------------------------------------------------------
race1_6      |
     Discrim |   1.899766   .0670124    28.35   0.000     1.768425    2.031108
        Diff |
        >=2  |  -1.472176   .0529271   -27.82   0.000    -1.575911    -1.36844
        >=3  |  -.6172409   .0360847   -17.11   0.000    -.6879655   -.5465163
        >=4  |   .4164753   .0344996    12.07   0.000     .3488573    .4840932
         =5  |    1.80158   .0617597    29.17   0.000     1.680534    1.922627
-------------+----------------------------------------------------------------
race1_8      |
     Discrim |   2.634189   .0926616    28.43   0.000     2.452575    2.815802
        Diff |
        >=2  |  -.5730361   .0319339   -17.94   0.000    -.6356254   -.5104468
        >=3  |   .1661737   .0288591     5.76   0.000     .1096109    .2227364
        >=4  |   .9642112   .0369396    26.10   0.000      .891811    1.036612
         =5  |   1.893646   .0579032    32.70   0.000     1.780158    2.007134
-------------+----------------------------------------------------------------
race2        |
     Discrim |   1.902225   .0739181    25.73   0.000     1.757348    2.047102
        Diff |
        >=2  |  -.0417386   .0328756    -1.27   0.204    -.1061735    .0226963
        >=3  |   .9709654   .0420131    23.11   0.000     .8886214     1.05331
        >=4  |   1.558951   .0563417    27.67   0.000     1.448523    1.669379
         =5  |   2.286068   .0819849    27.88   0.000     2.125381    2.446756
------------------------------------------------------------------------------

.                 predict white_id1       
(option pr assumed)
(option conditional(ebmeans) assumed)
(using 7 quadrature points)

. 
. reg fair i.panel##c.white_id if outcome==1, robust               

Linear regression                               Number of obs     =      1,264
                                                F(7, 1256)        =      38.88
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1895
                                                Root MSE          =     1.1917

----------------------------------------------------------------------------------
                 |               Robust
            fair |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
           panel |
              2  |   .2044458    .094631     2.16   0.031     .0187935    .3900982
              3  |   .0815994   .0949733     0.86   0.390    -.1047245    .2679232
              4  |    .585687   .0944496     6.20   0.000     .4003906    .7709833
                 |
        white_id |   .5975317   .0623441     9.58   0.000     .4752217    .7198418
                 |
panel#c.white_id |
              2  |  -.0083338   .0935719    -0.09   0.929    -.1919082    .1752406
              3  |  -.0646856   .0919185    -0.70   0.482    -.2450164    .1156452
              4  |  -.3410342   .0899135    -3.79   0.000    -.5174314    -.164637
                 |
           _cons |    2.88811   .0666108    43.36   0.000     2.757429     3.01879
----------------------------------------------------------------------------------

.         margins r.panel if panel==1 | panel==4, at(white_id=(-1.5(.5)2)) post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : white_id        =        -1.5

2._at        : white_id        =          -1

3._at        : white_id        =         -.5

4._at        : white_id        =           0

5._at        : white_id        =          .5

6._at        : white_id        =           1

7._at        : white_id        =         1.5

8._at        : white_id        =           2

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
   panel@_at |
 (4 vs 1) 1  |          1       41.64     0.0000
 (4 vs 1) 2  |          1       47.17     0.0000
 (4 vs 1) 3  |          1       49.54     0.0000
 (4 vs 1) 4  |          1       38.45     0.0000
 (4 vs 1) 5  |          1       16.67     0.0000
 (4 vs 1) 6  |          1        3.79     0.0519
 (4 vs 1) 7  |          1        0.22     0.6413
 (4 vs 1) 8  |          1        0.24     0.6250
      Joint  |          2       24.88     0.0000
             |
 Denominator |       1256
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
   panel@_at |
 (4 vs 1) 1  |   1.097238   .1700398      .7636449    1.430832
 (4 vs 1) 2  |   .9267211   .1349335      .6620013    1.191441
 (4 vs 1) 3  |   .7562041   .1074373      .5454277    .9669804
 (4 vs 1) 4  |    .585687   .0944496      .4003906    .7709833
 (4 vs 1) 5  |   .4151699   .1016902      .2156685    .6146712
 (4 vs 1) 6  |   .2446528   .1257111     -.0019741    .4912797
 (4 vs 1) 7  |   .0741357   .1590845      -.237965    .3862364
 (4 vs 1) 8  |  -.0963814    .197116     -.4830943    .2903315
--------------------------------------------------------------

.         estimates store fair1

. reg fair i.panel##c.white_id if outcome==2, robust               

Linear regression                               Number of obs     =      1,245
                                                F(7, 1237)        =      11.20
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0627
                                                Root MSE          =     1.1367

----------------------------------------------------------------------------------
                 |               Robust
            fair |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
           panel |
              2  |    .047955    .093178     0.51   0.607    -.1348494    .2307594
              3  |   .1676793   .0932043     1.80   0.072    -.0151767    .3505353
              4  |   .2398783   .0906404     2.65   0.008     .0620525    .4177041
                 |
        white_id |   -.206868   .0716873    -2.89   0.004    -.3475102   -.0662258
                 |
panel#c.white_id |
              2  |   .0426693   .1017436     0.42   0.675    -.1569397    .2422784
              3  |  -.0165076    .101764    -0.16   0.871    -.2161567    .1831414
              4  |  -.2432179   .0984239    -2.47   0.014    -.4363141   -.0501216
                 |
           _cons |   3.221749   .0666082    48.37   0.000     3.091072    3.352427
----------------------------------------------------------------------------------

.         margins r.panel if panel==1 | panel==4, at(white_id=(-1.5(.5)2)) post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : white_id        =        -1.5

2._at        : white_id        =          -1

3._at        : white_id        =         -.5

4._at        : white_id        =           0

5._at        : white_id        =          .5

6._at        : white_id        =           1

7._at        : white_id        =         1.5

8._at        : white_id        =           2

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
   panel@_at |
 (4 vs 1) 1  |          1       12.87     0.0003
 (4 vs 1) 2  |          1       13.86     0.0002
 (4 vs 1) 3  |          1       12.93     0.0003
 (4 vs 1) 4  |          1        7.00     0.0082
 (4 vs 1) 5  |          1        1.25     0.2634
 (4 vs 1) 6  |          1        0.00     0.9807
 (4 vs 1) 7  |          1        0.49     0.4823
 (4 vs 1) 8  |          1        1.24     0.2660
      Joint  |          2        6.97     0.0010
             |
 Denominator |       1237
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
   panel@_at |
 (4 vs 1) 1  |   .6047051   .1685602      .2740097    .9354005
 (4 vs 1) 2  |   .4830962    .129756      .2285301    .7376623
 (4 vs 1) 3  |   .3614872   .1005203      .1642782    .5586963
 (4 vs 1) 4  |   .2398783   .0906404      .0620525    .4177041
 (4 vs 1) 5  |   .1182694   .1056913     -.0890847    .3256234
 (4 vs 1) 6  |  -.0033396    .137729     -.2735478    .2668686
 (4 vs 1) 7  |  -.1249485   .1777963     -.4737641    .2238671
 (4 vs 1) 8  |  -.2465574   .2215793     -.6812703    .1881554
--------------------------------------------------------------

.         estimates store fair2   

. reg trust i.panel##c.white_id if outcome==1, robust              

Linear regression                               Number of obs     =      1,264
                                                F(7, 1256)        =      35.87
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1708
                                                Root MSE          =     1.2386

----------------------------------------------------------------------------------
                 |               Robust
           trust |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
           panel |
              2  |   .3599583   .1007724     3.57   0.000     .1622575    .5576591
              3  |   .1274789   .1013524     1.26   0.209    -.0713598    .3263175
              4  |   .4909279   .0999617     4.91   0.000     .2948176    .6870383
                 |
        white_id |   .5719899   .0732531     7.81   0.000     .4282779    .7157019
                 |
panel#c.white_id |
              2  |   .0363028   .1002917     0.36   0.717     -.160455    .2330606
              3  |  -.0906605   .0984787    -0.92   0.357    -.2838613    .1025404
              4  |    -.22931   .1008212    -2.27   0.023    -.4271065   -.0315136
                 |
           _cons |   2.940392   .0740336    39.72   0.000     2.795149    3.085635
----------------------------------------------------------------------------------

.         margins r.panel if panel==1 | panel==4, at(white_id=(-1.5(.5)2)) post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : white_id        =        -1.5

2._at        : white_id        =          -1

3._at        : white_id        =         -.5

4._at        : white_id        =           0

5._at        : white_id        =          .5

6._at        : white_id        =           1

7._at        : white_id        =         1.5

8._at        : white_id        =           2

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
   panel@_at |
 (4 vs 1) 1  |          1       20.70     0.0000
 (4 vs 1) 2  |          1       25.07     0.0000
 (4 vs 1) 3  |          1       28.65     0.0000
 (4 vs 1) 4  |          1       24.12     0.0000
 (4 vs 1) 5  |          1       11.54     0.0007
 (4 vs 1) 6  |          1        3.49     0.0620
 (4 vs 1) 7  |          1        0.67     0.4119
 (4 vs 1) 8  |          1        0.02     0.8847
      Joint  |          2       14.36     0.0000
             |
 Denominator |       1256
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
   panel@_at |
 (4 vs 1) 1  |    .834893    .183491      .4749104    1.194876
 (4 vs 1) 2  |    .720238   .1438551      .4380152    1.002461
 (4 vs 1) 3  |   .6055829   .1131463      .3836063    .8275596
 (4 vs 1) 4  |   .4909279   .0999617      .2948176    .6870383
 (4 vs 1) 5  |   .3762729   .1107477       .159002    .5935439
 (4 vs 1) 6  |   .2616179   .1400722     -.0131834    .5364192
 (4 vs 1) 7  |   .1469629    .179047     -.2043013    .4982271
 (4 vs 1) 8  |   .0323079    .222661     -.4045205    .4691363
--------------------------------------------------------------

.         estimates store trust1

. reg trust i.panel##c.white_id if outcome==2, robust              

Linear regression                               Number of obs     =      1,245
                                                F(7, 1237)        =      11.45
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0715
                                                Root MSE          =     1.1601

----------------------------------------------------------------------------------
                 |               Robust
           trust |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
           panel |
              2  |   .0599197   .0953248     0.63   0.530    -.1270964    .2469359
              3  |   .1555038   .0956153     1.63   0.104    -.0320824    .3430899
              4  |   .3034922   .0938858     3.23   0.001     .1192991    .4876853
                 |
        white_id |  -.3043727   .0764132    -3.98   0.000    -.4542866   -.1544588
                 |
panel#c.white_id |
              2  |   .0845173   .1058194     0.80   0.425    -.1230881    .2921227
              3  |  -.0274881   .1092805    -0.25   0.801    -.2418837    .1869076
              4  |  -.0596336   .1067157    -0.56   0.576    -.2689974    .1497302
                 |
           _cons |   3.352433   .0696399    48.14   0.000     3.215807    3.489058
----------------------------------------------------------------------------------

.         margins r.panel if panel==1 | panel==4, at(white_id=(-1.5(.5)2)) post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : white_id        =        -1.5

2._at        : white_id        =          -1

3._at        : white_id        =         -.5

4._at        : white_id        =           0

5._at        : white_id        =          .5

6._at        : white_id        =           1

7._at        : white_id        =         1.5

8._at        : white_id        =           2

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
   panel@_at |
 (4 vs 1) 1  |          1        4.60     0.0322
 (4 vs 1) 2  |          1        6.72     0.0097
 (4 vs 1) 3  |          1        9.77     0.0018
 (4 vs 1) 4  |          1       10.45     0.0013
 (4 vs 1) 5  |          1        6.27     0.0124
 (4 vs 1) 6  |          1        2.86     0.0909
 (4 vs 1) 7  |          1        1.30     0.2548
 (4 vs 1) 8  |          1        0.61     0.4344
      Joint  |          2        5.44     0.0045
             |
 Denominator |       1237
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
   panel@_at |
 (4 vs 1) 1  |   .3929426    .183246      .0334352      .75245
 (4 vs 1) 2  |   .3631258   .1401076       .088251    .6380006
 (4 vs 1) 3  |    .333309    .106655      .1240644    .5425536
 (4 vs 1) 4  |   .3034922   .0938858      .1192991    .4876853
 (4 vs 1) 5  |   .2736754   .1093066      .0592285    .4881223
 (4 vs 1) 6  |   .2438586   .1441369     -.0389212    .5266384
 (4 vs 1) 7  |   .2140418   .1878751     -.1545473     .582631
 (4 vs 1) 8  |    .184225   .2356116     -.2780175    .6464675
--------------------------------------------------------------

.         estimates store trust2

. reg fair i.panel##c.immigration if outcome==1, robust            

Linear regression                               Number of obs     =      1,268
                                                F(7, 1260)        =      26.18
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1335
                                                Root MSE          =     1.2322

-------------------------------------------------------------------------------------
                    |               Robust
               fair |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
              panel |
                 2  |   .1879902   .0979103     1.92   0.055    -.0040951    .3800754
                 3  |    .111669   .0985671     1.13   0.257    -.0817048    .3050428
                 4  |   .5872935   .0969648     6.06   0.000     .3970633    .7775238
                    |
        immigration |   .5590758   .0698094     8.01   0.000     .4221203    .6960312
                    |
panel#c.immigration |
                 2  |  -.0575109   .1007375    -0.57   0.568    -.2551425    .1401208
                 3  |  -.2006256   .1043571    -1.92   0.055    -.4053583    .0041072
                 4  |   -.371826   .1005935    -3.70   0.000    -.5691752   -.1744767
                    |
              _cons |    2.88998    .068796    42.01   0.000     2.755013    3.024947
-------------------------------------------------------------------------------------

.         margins r.panel if panel==1 | panel==4, at(immigration=(-1.5(.5)2)) post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : immigration     =        -1.5

2._at        : immigration     =          -1

3._at        : immigration     =         -.5

4._at        : immigration     =           0

5._at        : immigration     =          .5

6._at        : immigration     =           1

7._at        : immigration     =         1.5

8._at        : immigration     =           2

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
   panel@_at |
 (4 vs 1) 1  |          1       45.09     0.0000
 (4 vs 1) 2  |          1       52.68     0.0000
 (4 vs 1) 3  |          1       54.84     0.0000
 (4 vs 1) 4  |          1       36.68     0.0000
 (4 vs 1) 5  |          1       12.43     0.0004
 (4 vs 1) 6  |          1        2.15     0.1427
 (4 vs 1) 7  |          1        0.02     0.8750
 (4 vs 1) 8  |          1        0.45     0.5012
      Joint  |          2       27.85     0.0000
             |
 Denominator |       1260
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
   panel@_at |
 (4 vs 1) 1  |   1.145032   .1705263      .8104857    1.479579
 (4 vs 1) 2  |   .9591195   .1321388      .6998831    1.218356
 (4 vs 1) 3  |   .7732065    .104411      .5683679    .9780451
 (4 vs 1) 4  |   .5872935   .0969648      .3970633    .7775238
 (4 vs 1) 5  |   .4013806   .1138517      .1780207    .6247404
 (4 vs 1) 6  |   .2154676   .1469074     -.0727425    .5036776
 (4 vs 1) 7  |   .0295546   .1877788      -.338839    .3979482
 (4 vs 1) 8  |  -.1563584   .2323779     -.6122487    .2995319
--------------------------------------------------------------

.         estimates store fair3

. reg fair i.panel##c.immigration if outcome==2, robust            

Linear regression                               Number of obs     =      1,249
                                                F(7, 1241)        =      16.70
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0971
                                                Root MSE          =     1.1177

-------------------------------------------------------------------------------------
                    |               Robust
               fair |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
              panel |
                 2  |   .0688154   .0905662     0.76   0.447    -.1088643    .2464951
                 3  |   .2138375   .0901144     2.37   0.018     .0370441    .3906309
                 4  |   .2674961   .0883665     3.03   0.003     .0941318    .4408604
                    |
        immigration |  -.3751913   .0636656    -5.89   0.000    -.5000953   -.2502872
                    |
panel#c.immigration |
                 2  |   .1130666   .0922847     1.23   0.221    -.0679847    .2941178
                 3  |   .0898515   .0970517     0.93   0.355    -.1005519     .280255
                 4  |  -.0786581   .0976692    -0.81   0.421    -.2702731    .1129569
                    |
              _cons |   3.204335   .0636331    50.36   0.000     3.079494    3.329175
-------------------------------------------------------------------------------------

.         margins r.panel if panel==1 | panel==4, at(immigration=(-1.5(.5)2)) post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : immigration     =        -1.5

2._at        : immigration     =          -1

3._at        : immigration     =         -.5

4._at        : immigration     =           0

5._at        : immigration     =          .5

6._at        : immigration     =           1

7._at        : immigration     =         1.5

8._at        : immigration     =           2

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
   panel@_at |
 (4 vs 1) 1  |          1        5.60     0.0181
 (4 vs 1) 2  |          1        7.71     0.0056
 (4 vs 1) 3  |          1       10.14     0.0015
 (4 vs 1) 4  |          1        9.16     0.0025
 (4 vs 1) 5  |          1        4.69     0.0305
 (4 vs 1) 6  |          1        1.86     0.1728
 (4 vs 1) 7  |          1        0.70     0.4034
 (4 vs 1) 8  |          1        0.24     0.6209
      Joint  |          2        5.22     0.0055
             |
 Denominator |       1241
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
   panel@_at |
 (4 vs 1) 1  |   .3854833   .1629442      .0658067    .7051599
 (4 vs 1) 2  |   .3461542   .1246346      .1016364    .5906721
 (4 vs 1) 3  |   .3068252    .096366       .117767    .4958834
 (4 vs 1) 4  |   .2674961   .0883665      .0941318    .4408604
 (4 vs 1) 5  |   .2281671    .105359      .0214656    .4348685
 (4 vs 1) 6  |    .188838    .138427     -.0827388    .4604148
 (4 vs 1) 7  |    .149509   .1788664      -.201405    .5004229
 (4 vs 1) 8  |   .1101799   .2226971     -.3267244    .5470842
--------------------------------------------------------------

.         estimates store fair4   

. reg fair i.panel##c.immigration if outcome==1, robust            

Linear regression                               Number of obs     =      1,268
                                                F(7, 1260)        =      26.18
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1335
                                                Root MSE          =     1.2322

-------------------------------------------------------------------------------------
                    |               Robust
               fair |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
              panel |
                 2  |   .1879902   .0979103     1.92   0.055    -.0040951    .3800754
                 3  |    .111669   .0985671     1.13   0.257    -.0817048    .3050428
                 4  |   .5872935   .0969648     6.06   0.000     .3970633    .7775238
                    |
        immigration |   .5590758   .0698094     8.01   0.000     .4221203    .6960312
                    |
panel#c.immigration |
                 2  |  -.0575109   .1007375    -0.57   0.568    -.2551425    .1401208
                 3  |  -.2006256   .1043571    -1.92   0.055    -.4053583    .0041072
                 4  |   -.371826   .1005935    -3.70   0.000    -.5691752   -.1744767
                    |
              _cons |    2.88998    .068796    42.01   0.000     2.755013    3.024947
-------------------------------------------------------------------------------------

.         margins r.panel if panel==1 | panel==4, at(immigration=(-1.5(.5)2)) post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : immigration     =        -1.5

2._at        : immigration     =          -1

3._at        : immigration     =         -.5

4._at        : immigration     =           0

5._at        : immigration     =          .5

6._at        : immigration     =           1

7._at        : immigration     =         1.5

8._at        : immigration     =           2

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
   panel@_at |
 (4 vs 1) 1  |          1       45.09     0.0000
 (4 vs 1) 2  |          1       52.68     0.0000
 (4 vs 1) 3  |          1       54.84     0.0000
 (4 vs 1) 4  |          1       36.68     0.0000
 (4 vs 1) 5  |          1       12.43     0.0004
 (4 vs 1) 6  |          1        2.15     0.1427
 (4 vs 1) 7  |          1        0.02     0.8750
 (4 vs 1) 8  |          1        0.45     0.5012
      Joint  |          2       27.85     0.0000
             |
 Denominator |       1260
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
   panel@_at |
 (4 vs 1) 1  |   1.145032   .1705263      .8104857    1.479579
 (4 vs 1) 2  |   .9591195   .1321388      .6998831    1.218356
 (4 vs 1) 3  |   .7732065    .104411      .5683679    .9780451
 (4 vs 1) 4  |   .5872935   .0969648      .3970633    .7775238
 (4 vs 1) 5  |   .4013806   .1138517      .1780207    .6247404
 (4 vs 1) 6  |   .2154676   .1469074     -.0727425    .5036776
 (4 vs 1) 7  |   .0295546   .1877788      -.338839    .3979482
 (4 vs 1) 8  |  -.1563584   .2323779     -.6122487    .2995319
--------------------------------------------------------------

.         estimates store trust3

. reg fair i.panel##c.immigration if outcome==2, robust            

Linear regression                               Number of obs     =      1,249
                                                F(7, 1241)        =      16.70
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0971
                                                Root MSE          =     1.1177

-------------------------------------------------------------------------------------
                    |               Robust
               fair |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
              panel |
                 2  |   .0688154   .0905662     0.76   0.447    -.1088643    .2464951
                 3  |   .2138375   .0901144     2.37   0.018     .0370441    .3906309
                 4  |   .2674961   .0883665     3.03   0.003     .0941318    .4408604
                    |
        immigration |  -.3751913   .0636656    -5.89   0.000    -.5000953   -.2502872
                    |
panel#c.immigration |
                 2  |   .1130666   .0922847     1.23   0.221    -.0679847    .2941178
                 3  |   .0898515   .0970517     0.93   0.355    -.1005519     .280255
                 4  |  -.0786581   .0976692    -0.81   0.421    -.2702731    .1129569
                    |
              _cons |   3.204335   .0636331    50.36   0.000     3.079494    3.329175
-------------------------------------------------------------------------------------

.         margins r.panel if panel==1 | panel==4, at(immigration=(-1.5(.5)2)) post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : immigration     =        -1.5

2._at        : immigration     =          -1

3._at        : immigration     =         -.5

4._at        : immigration     =           0

5._at        : immigration     =          .5

6._at        : immigration     =           1

7._at        : immigration     =         1.5

8._at        : immigration     =           2

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
   panel@_at |
 (4 vs 1) 1  |          1        5.60     0.0181
 (4 vs 1) 2  |          1        7.71     0.0056
 (4 vs 1) 3  |          1       10.14     0.0015
 (4 vs 1) 4  |          1        9.16     0.0025
 (4 vs 1) 5  |          1        4.69     0.0305
 (4 vs 1) 6  |          1        1.86     0.1728
 (4 vs 1) 7  |          1        0.70     0.4034
 (4 vs 1) 8  |          1        0.24     0.6209
      Joint  |          2        5.22     0.0055
             |
 Denominator |       1241
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
   panel@_at |
 (4 vs 1) 1  |   .3854833   .1629442      .0658067    .7051599
 (4 vs 1) 2  |   .3461542   .1246346      .1016364    .5906721
 (4 vs 1) 3  |   .3068252    .096366       .117767    .4958834
 (4 vs 1) 4  |   .2674961   .0883665      .0941318    .4408604
 (4 vs 1) 5  |   .2281671    .105359      .0214656    .4348685
 (4 vs 1) 6  |    .188838    .138427     -.0827388    .4604148
 (4 vs 1) 7  |    .149509   .1788664      -.201405    .5004229
 (4 vs 1) 8  |   .1101799   .2226971     -.3267244    .5470842
--------------------------------------------------------------

.         estimates store trust4          

.         
. coefplot fair1, bylabel("Non-critical Report of U.S.") || ///
>                  fair2, bylabel("Critical Report of U.S.") ///
>                  yline(0) scheme(plottig) vertical ciopts(recast(rcap)) ///
>                  xtitle("White Nationalism") title("UNHCR is Fair") ///
>                  xlabel(1 "-1.5" 2 "-1" 3 "-0.5" 4 "0" 5 "0.5" 6 "1" 7 "1.5" 8 "2") ///
>                  ytitle("Marginal Effect")              
(note:  clockdir by_legend_position not found in scheme, default attributes used)

.                  graph save "wnat1.gph", replace
(note: file wnat1.gph not found)
(file wnat1.gph saved)

. 
. coefplot trust1, bylabel("Non-critical Report of U.S.") || ///
>                  trust2, bylabel("Critical Report of U.S.") ///
>                  yline(0) scheme(plottig) vertical ciopts(recast(rcap)) ///
>                  xtitle("White Nationalism") title("UNHCR can be Trusted") ///
>                  xlabel(1 "-1.5" 2 "-1" 3 "-0.5" 4 "0" 5 "0.5" 6 "1" 7 "1.5" 8 "2") ///
>                  ytitle("Marginal Effect")              
(note:  clockdir by_legend_position not found in scheme, default attributes used)

.                  graph save "wnat2.gph", replace
(note: file wnat2.gph not found)
(file wnat2.gph saved)

. 
. graph combine "wnat1.gph" "wnat2.gph", ///
>           xcommon ycommon title("White Nationalism", size(med)) name(white1)
(note:  clockdir by_legend_position not found in scheme, default attributes used)
(note:  clockdir by_legend_position not found in scheme, default attributes used)
(note:  clockdir by_legend_position not found in scheme, default attributes used)
(note:  named style med not found in class gsize, default attributes used)

.           
. coefplot fair3, bylabel("Non-critical Report of U.S.") || ///
>                  fair4, bylabel("Critical Report of U.S.") ///
>                  yline(0) scheme(plottig) vertical ciopts(recast(rcap)) ///
>                  xtitle("Anti-Immigration Attitudes") title("UNHCR is Fair") ///
>                  xlabel(1 "-1.5" 2 "-1" 3 "-0.5" 4 "0" 5 "0.5" 6 "1" 7 "1.5" 8 "2") ///
>                  ytitle("Marginal Effect")              
(note:  clockdir by_legend_position not found in scheme, default attributes used)

.                  graph save "imm1.gph", replace
(note: file imm1.gph not found)
(file imm1.gph saved)

. 
. coefplot trust3, bylabel("Non-critical Report of U.S.") || ///
>                  trust4, bylabel("Critical Report of U.S.") ///
>                  yline(0) scheme(plottig) vertical ciopts(recast(rcap)) ///
>                  xtitle("Anti-Immigration Attitudes") title("UNHCR can be Trusted") ///
>                  xlabel(1 "-1.5" 2 "-1" 3 "-0.5" 4 "0" 5 "0.5" 6 "1" 7 "1.5" 8 "2") ///
>                  ytitle("Marginal Effect")              
(note:  clockdir by_legend_position not found in scheme, default attributes used)

.                  graph save "imm2.gph", replace
(note: file imm2.gph not found)
(file imm2.gph saved)

. 
. graph combine "imm1.gph" "imm2.gph", ///
>           xcommon ycommon title("Anti-Immigration Attitudes", size(med)) name(imm1)              
(note:  clockdir by_legend_position not found in scheme, default attributes used)
(note:  clockdir by_legend_position not found in scheme, default attributes used)
(note:  clockdir by_legend_position not found in scheme, default attributes used)
(note:  named style med not found in class gsize, default attributes used)

. 
. graph combine imm1 white1, rows(2) scale(1.4)     
(note:  clockdir by_legend_position not found in scheme, default attributes used)
(note:  clockdir by_legend_position not found in scheme, default attributes used)
(note:  clockdir by_legend_position not found in scheme, default attributes used)
(note:  clockdir by_legend_position not found in scheme, default attributes used)
(note:  clockdir by_legend_position not found in scheme, default attributes used)
(note:  named style med not found in class gsize, default attributes used)
(note:  clockdir by_legend_position not found in scheme, default attributes used)
(note:  clockdir by_legend_position not found in scheme, default attributes used)
(note:  clockdir by_legend_position not found in scheme, default attributes used)
(note:  clockdir by_legend_position not found in scheme, default attributes used)
(note:  named style med not found in class gsize, default attributes used)

.                                                                 
. *** Figure A10: UNHCR Panel Diversity and Substantive Legitimacy, by Political 
. 
. reg refugee i.panel##c.white_id if outcome==1, robust            

Linear regression                               Number of obs     =      1,264
                                                F(7, 1256)        =      37.33
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1763
                                                Root MSE          =     1.3153

----------------------------------------------------------------------------------
                 |               Robust
         refugee |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
           panel |
              2  |   .2194688    .105843     2.07   0.038     .0118203    .4271174
              3  |   .0268237   .1063228     0.25   0.801    -.1817661    .2354135
              4  |     .26503   .1063262     2.49   0.013     .0564335    .4736265
                 |
        white_id |   .6053057   .0707064     8.56   0.000       .46659    .7440214
                 |
panel#c.white_id |
              2  |   .0363704   .1013131     0.36   0.720    -.1623911    .2351318
              3  |  -.0351522   .1023339    -0.34   0.731    -.2359165    .1656121
              4  |  -.1030238   .1054493    -0.98   0.329       -.3099    .1038524
                 |
           _cons |   3.097038   .0768897    40.28   0.000     2.946192    3.247885
----------------------------------------------------------------------------------

.         margins r.panel if panel==1 | panel==4, at(white_id=(-1.5(.5)2)) post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : white_id        =        -1.5

2._at        : white_id        =          -1

3._at        : white_id        =         -.5

4._at        : white_id        =           0

5._at        : white_id        =          .5

6._at        : white_id        =           1

7._at        : white_id        =         1.5

8._at        : white_id        =           2

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
   panel@_at |
 (4 vs 1) 1  |          1        4.54     0.0333
 (4 vs 1) 2  |          1        5.63     0.0178
 (4 vs 1) 3  |          1        6.73     0.0096
 (4 vs 1) 4  |          1        6.21     0.0128
 (4 vs 1) 5  |          1        3.43     0.0641
 (4 vs 1) 6  |          1        1.26     0.2616
 (4 vs 1) 7  |          1        0.36     0.5485
 (4 vs 1) 8  |          1        0.07     0.7970
      Joint  |          2        3.43     0.0328
             |
 Denominator |       1256
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
   panel@_at |
 (4 vs 1) 1  |   .4195657   .1968567      .0333615    .8057699
 (4 vs 1) 2  |   .3680538   .1550602      .0638482    .6722594
 (4 vs 1) 3  |   .3165419   .1220433      .0771106    .5559732
 (4 vs 1) 4  |     .26503   .1063262      .0564335    .4736265
 (4 vs 1) 5  |   .2135181   .1152203     -.0125273    .4395635
 (4 vs 1) 6  |   .1620062   .1442426     -.1209769    .4449893
 (4 vs 1) 7  |   .1104943   .1841085     -.2506998    .4716885
 (4 vs 1) 8  |   .0589824   .2292286     -.3907307    .5086955
--------------------------------------------------------------

.         estimates store refugee1

. reg refugee i.panel##c.white_id if outcome==2, robust            

Linear regression                               Number of obs     =      1,245
                                                F(7, 1237)        =      24.68
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1413
                                                Root MSE          =     1.1812

----------------------------------------------------------------------------------
                 |               Robust
         refugee |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
           panel |
              2  |  -.0778315   .0960097    -0.81   0.418    -.2661913    .1105284
              3  |  -.1243222     .09746    -1.28   0.202    -.3155273    .0668829
              4  |   .0711187   .0941476     0.76   0.450     -.113588    .2558254
                 |
        white_id |  -.4748624   .0733399    -6.47   0.000    -.6187467   -.3309781
                 |
panel#c.white_id |
              2  |   .0004939   .1038824     0.00   0.996    -.2033112    .2042991
              3  |  -.0017848   .1102807    -0.02   0.987    -.2181427    .2145732
              4  |   -.021166   .1043601    -0.20   0.839    -.2259083    .1835763
                 |
           _cons |   3.817245   .0692874    55.09   0.000     3.681311    3.953179
----------------------------------------------------------------------------------

.         margins r.panel if panel==1 | panel==4, at(white_id=(-1.5(.5)2)) post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : white_id        =        -1.5

2._at        : white_id        =          -1

3._at        : white_id        =         -.5

4._at        : white_id        =           0

5._at        : white_id        =          .5

6._at        : white_id        =           1

7._at        : white_id        =         1.5

8._at        : white_id        =           2

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
   panel@_at |
 (4 vs 1) 1  |          1        0.37     0.5427
 (4 vs 1) 2  |          1        0.52     0.4731
 (4 vs 1) 3  |          1        0.67     0.4135
 (4 vs 1) 4  |          1        0.57     0.4502
 (4 vs 1) 5  |          1        0.28     0.5983
 (4 vs 1) 6  |          1        0.11     0.7418
 (4 vs 1) 7  |          1        0.04     0.8404
 (4 vs 1) 8  |          1        0.01     0.9056
      Joint  |          2        0.34     0.7118
             |
 Denominator |       1237
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
   panel@_at |
 (4 vs 1) 1  |   .1028677   .1689351     -.2285632    .4342986
 (4 vs 1) 2  |   .0922847   .1285889     -.1599919    .3445612
 (4 vs 1) 3  |   .0817017   .0998834     -.1142579    .2776613
 (4 vs 1) 4  |   .0711187   .0941476      -.113588    .2558254
 (4 vs 1) 5  |   .0605356   .1148755     -.1648367     .285908
 (4 vs 1) 6  |   .0499526   .1515734     -.2474167    .3473219
 (4 vs 1) 7  |   .0393696   .1954434     -.3440675    .4228068
 (4 vs 1) 8  |   .0287866   .2426257     -.4472168    .5047901
--------------------------------------------------------------

.         estimates store refugee2

. reg american i.panel##c.white_id if outcome==1, robust           

Linear regression                               Number of obs     =      1,264
                                                F(7, 1256)        =      41.69
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1846
                                                Root MSE          =     1.3102

----------------------------------------------------------------------------------
                 |               Robust
        american |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
           panel |
              2  |   .2081939   .1060062     1.96   0.050     .0002251    .4161626
              3  |   .0968477   .1063939     0.91   0.363    -.1118816    .3055771
              4  |   .3617295   .1047465     3.45   0.001     .1562322    .5672268
                 |
        white_id |   .5934691   .0712008     8.34   0.000     .4537834    .7331547
                 |
panel#c.white_id |
              2  |   .0542843   .1029985     0.53   0.598    -.1477837    .2563524
              3  |    .025951   .1006151     0.26   0.797    -.1714412    .2233432
              4  |  -.0900688   .0976242    -0.92   0.356    -.2815933    .1014556
                 |
           _cons |   3.193298   .0767962    41.58   0.000     3.042635    3.343961
----------------------------------------------------------------------------------

.         margins r.panel if panel==1 | panel==4, at(white_id=(-1.5(.5)2)) post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : white_id        =        -1.5

2._at        : white_id        =          -1

3._at        : white_id        =         -.5

4._at        : white_id        =           0

5._at        : white_id        =          .5

6._at        : white_id        =           1

7._at        : white_id        =         1.5

8._at        : white_id        =           2

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
   panel@_at |
 (4 vs 1) 1  |          1        6.61     0.0103
 (4 vs 1) 2  |          1        8.58     0.0035
 (4 vs 1) 3  |          1       11.03     0.0009
 (4 vs 1) 4  |          1       11.93     0.0006
 (4 vs 1) 5  |          1        8.57     0.0035
 (4 vs 1) 6  |          1        4.29     0.0386
 (4 vs 1) 7  |          1        1.87     0.1718
 (4 vs 1) 8  |          1        0.78     0.3786
      Joint  |          2        6.03     0.0025
             |
 Denominator |       1256
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
   panel@_at |
 (4 vs 1) 1  |   .4968328   .1932861      .1176335     .876032
 (4 vs 1) 2  |   .4517983   .1542672      .1491485    .7544482
 (4 vs 1) 3  |   .4067639   .1224846      .1664669    .6470609
 (4 vs 1) 4  |   .3617295   .1047465      .1562322    .5672268
 (4 vs 1) 5  |   .3166951   .1081961        .10443    .5289601
 (4 vs 1) 6  |   .2716606   .1311725      .0143192    .5290021
 (4 vs 1) 7  |   .2266262   .1657447     -.0985408    .5517932
 (4 vs 1) 8  |   .1815918   .2061592     -.2228626    .5860462
--------------------------------------------------------------

.         estimates store american1

. reg american i.panel##c.white_id if outcome==2, robust           

Linear regression                               Number of obs     =      1,245
                                                F(7, 1237)        =      34.01
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1788
                                                Root MSE          =     1.2242

----------------------------------------------------------------------------------
                 |               Robust
        american |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
           panel |
              2  |  -.0449996   .0987212    -0.46   0.649    -.2386791    .1486799
              3  |  -.0737563   .0988892    -0.75   0.456    -.2677654    .1202528
              4  |    .096374   .0977996     0.99   0.325    -.0954974    .2882453
                 |
        white_id |  -.6382625   .0738892    -8.64   0.000    -.7832244   -.4933005
                 |
panel#c.white_id |
              2  |   .0753449   .1050631     0.72   0.473    -.1307766    .2814664
              3  |   .1305748   .1119226     1.17   0.244    -.0890043     .350154
              4  |   .0429718   .1070942     0.40   0.688    -.1671346    .2530782
                 |
           _cons |   3.661382   .0698187    52.44   0.000     3.524406    3.798358
----------------------------------------------------------------------------------

.         margins r.panel if panel==1 | panel==4, at(white_id=(-1.5(.5)2)) post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : white_id        =        -1.5

2._at        : white_id        =          -1

3._at        : white_id        =         -.5

4._at        : white_id        =           0

5._at        : white_id        =          .5

6._at        : white_id        =           1

7._at        : white_id        =         1.5

8._at        : white_id        =           2

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
   panel@_at |
 (4 vs 1) 1  |          1        0.04     0.8514
 (4 vs 1) 2  |          1        0.17     0.6805
 (4 vs 1) 3  |          1        0.54     0.4611
 (4 vs 1) 4  |          1        0.97     0.3246
 (4 vs 1) 5  |          1        0.95     0.3287
 (4 vs 1) 6  |          1        0.77     0.3808
 (4 vs 1) 7  |          1        0.62     0.4312
 (4 vs 1) 8  |          1        0.52     0.4709
      Joint  |          2        0.51     0.6025
             |
 Denominator |       1237
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
   panel@_at |
 (4 vs 1) 1  |   .0319162   .1703756     -.3023408    .3661733
 (4 vs 1) 2  |   .0534021   .1296363     -.2009291    .3077334
 (4 vs 1) 3  |   .0748881   .1015769      -.124394    .2741701
 (4 vs 1) 4  |    .096374   .0977996     -.0954974    .2882453
 (4 vs 1) 5  |   .1178599   .1206077     -.1187585    .3544782
 (4 vs 1) 6  |   .1393458   .1589411     -.1724782    .4511698
 (4 vs 1) 7  |   .1608317   .2042374     -.2398584    .5615218
 (4 vs 1) 8  |   .1823176   .2527809      -.313609    .6782442
--------------------------------------------------------------

.         estimates store american2

. reg refugee i.panel##c.immigration if outcome==1, robust                 

Linear regression                               Number of obs     =      1,268
                                                F(7, 1260)        =      37.72
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1676
                                                Root MSE          =     1.3233

-------------------------------------------------------------------------------------
                    |               Robust
            refugee |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
              panel |
                 2  |   .1895711   .1054209     1.80   0.072    -.0172487    .3963909
                 3  |   .0579548   .1069961     0.54   0.588    -.1519553    .2678648
                 4  |   .2719421     .10692     2.54   0.011     .0621814    .4817029
                    |
        immigration |   .6393132    .074853     8.54   0.000     .4924628    .7861635
                    |
panel#c.immigration |
                 2  |   .0564093    .101303     0.56   0.578    -.1423319    .2551505
                 3  |  -.1570858   .1136997    -1.38   0.167    -.3801475    .0659758
                 4  |    -.15959   .1073552    -1.49   0.137    -.3702046    .0510247
                    |
              _cons |    3.10409   .0770101    40.31   0.000     2.953008    3.255172
-------------------------------------------------------------------------------------

.         margins r.panel if panel==1 | panel==4, at(immigration=(-1.5(.5)2)) post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : immigration     =        -1.5

2._at        : immigration     =          -1

3._at        : immigration     =         -.5

4._at        : immigration     =           0

5._at        : immigration     =          .5

6._at        : immigration     =           1

7._at        : immigration     =         1.5

8._at        : immigration     =           2

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
   panel@_at |
 (4 vs 1) 1  |          1        7.19     0.0074
 (4 vs 1) 2  |          1        8.36     0.0039
 (4 vs 1) 3  |          1        8.85     0.0030
 (4 vs 1) 4  |          1        6.47     0.0111
 (4 vs 1) 5  |          1        2.52     0.1126
 (4 vs 1) 6  |          1        0.53     0.4650
 (4 vs 1) 7  |          1        0.03     0.8680
 (4 vs 1) 8  |          1        0.04     0.8457
      Joint  |          2        4.45     0.0118
             |
 Denominator |       1260
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
   panel@_at |
 (4 vs 1) 1  |   .5113271   .1906648      .1372717    .8853825
 (4 vs 1) 2  |   .4315321   .1492764      .1386745    .7243897
 (4 vs 1) 3  |   .3517371   .1182217      .1198041    .5836702
 (4 vs 1) 4  |   .2719421     .10692      .0621814    .4817029
 (4 vs 1) 5  |   .1921472    .121037     -.0453091    .4296034
 (4 vs 1) 6  |   .1123522   .1537225     -.1892281    .4139324
 (4 vs 1) 7  |   .0325572   .1958924      -.351754    .4168684
 (4 vs 1) 8  |  -.0472378   .2426512     -.5232827    .4288071
--------------------------------------------------------------

.         estimates store refugee3

. reg refugee i.panel##c.immigration if outcome==2, robust                 

Linear regression                               Number of obs     =      1,249
                                                F(7, 1241)        =      43.68
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2182
                                                Root MSE          =     1.1284

-------------------------------------------------------------------------------------
                    |               Robust
            refugee |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
              panel |
                 2  |  -.0310714   .0890992    -0.35   0.727    -.2058732    .1437304
                 3  |  -.0657391   .0917882    -0.72   0.474    -.2458164    .1143382
                 4  |   .1022864   .0902783     1.13   0.257    -.0748284    .2794013
                    |
        immigration |  -.6064546   .0673163    -9.01   0.000    -.7385209   -.4743883
                    |
panel#c.immigration |
                 2  |  -.0372197   .0900441    -0.41   0.679    -.2138751    .1394358
                 3  |   .0360416   .0997173     0.36   0.718    -.1595915    .2316748
                 4  |   .0944638   .1042689     0.91   0.365     -.110099    .2990266
                    |
              _cons |   3.795603   .0646338    58.72   0.000     3.668799    3.922406
-------------------------------------------------------------------------------------

.         margins r.panel if panel==1 | panel==4, at(immigration=(-1.5(.5)2)) post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : immigration     =        -1.5

2._at        : immigration     =          -1

3._at        : immigration     =         -.5

4._at        : immigration     =           0

5._at        : immigration     =          .5

6._at        : immigration     =           1

7._at        : immigration     =         1.5

8._at        : immigration     =           2

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
   panel@_at |
 (4 vs 1) 1  |          1        0.06     0.8048
 (4 vs 1) 2  |          1        0.00     0.9477
 (4 vs 1) 3  |          1        0.36     0.5498
 (4 vs 1) 4  |          1        1.28     0.2574
 (4 vs 1) 5  |          1        1.69     0.1945
 (4 vs 1) 6  |          1        1.63     0.2026
 (4 vs 1) 7  |          1        1.50     0.2216
 (4 vs 1) 8  |          1        1.38     0.2395
      Joint  |          2        0.85     0.4295
             |
 Denominator |       1241
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
   panel@_at |
 (4 vs 1) 1  |  -.0394092   .1594254     -.3521824    .2733639
 (4 vs 1) 2  |   .0078226   .1192688      -.226168    .2418133
 (4 vs 1) 3  |   .0550545   .0920304     -.1254979    .2356069
 (4 vs 1) 4  |   .1022864   .0902783     -.0748284    .2794013
 (4 vs 1) 5  |   .1495183   .1151813     -.0764533    .3754899
 (4 vs 1) 6  |   .1967502    .154335     -.1060362    .4995365
 (4 vs 1) 7  |   .2439821   .1995191     -.1474499     .635414
 (4 vs 1) 8  |   .2912139   .2474519     -.1942563    .7766842
--------------------------------------------------------------

.         estimates store refugee4                                

. reg american i.panel##c.immigration if outcome==1, robust                

Linear regression                               Number of obs     =      1,268
                                                F(7, 1260)        =      34.14
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1576
                                                Root MSE          =     1.3314

-------------------------------------------------------------------------------------
                    |               Robust
           american |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
              panel |
                 2  |   .1733902   .1070274     1.62   0.105    -.0365814    .3833618
                 3  |   .1257864   .1081688     1.16   0.245    -.0864244    .3379972
                 4  |   .3639175   .1060231     3.43   0.001     .1559163    .5719187
                    |
        immigration |   .5925682   .0780487     7.59   0.000     .4394485    .7456879
                    |
panel#c.immigration |
                 2  |   .0635873   .1050664     0.61   0.545     -.142537    .2697117
                 3  |  -.0839464   .1165329    -0.72   0.471    -.3125663    .1446735
                 4  |  -.1284718   .1062958    -1.21   0.227    -.3370081    .0800644
                    |
              _cons |   3.204867   .0775907    41.30   0.000     3.052646    3.357089
-------------------------------------------------------------------------------------

.         margins r.panel if panel==1 | panel==4, at(immigration=(-1.5(.5)2)) post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : immigration     =        -1.5

2._at        : immigration     =          -1

3._at        : immigration     =         -.5

4._at        : immigration     =           0

5._at        : immigration     =          .5

6._at        : immigration     =           1

7._at        : immigration     =         1.5

8._at        : immigration     =           2

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
   panel@_at |
 (4 vs 1) 1  |          1        8.43     0.0038
 (4 vs 1) 2  |          1       10.73     0.0011
 (4 vs 1) 3  |          1       13.00     0.0003
 (4 vs 1) 4  |          1       11.78     0.0006
 (4 vs 1) 5  |          1        6.40     0.0115
 (4 vs 1) 6  |          1        2.47     0.1165
 (4 vs 1) 7  |          1        0.80     0.3708
 (4 vs 1) 8  |          1        0.20     0.6522
      Joint  |          2        6.61     0.0014
             |
 Denominator |       1260
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
   panel@_at |
 (4 vs 1) 1  |   .5566253   .1917289      .1804823    .9327683
 (4 vs 1) 2  |   .4923893    .150347      .1974314    .7873473
 (4 vs 1) 3  |   .4281534   .1187344      .1952146    .6610923
 (4 vs 1) 4  |   .3639175   .1060231      .1559163    .5719187
 (4 vs 1) 5  |   .2996816   .1184624      .0672764    .5320868
 (4 vs 1) 6  |   .2354457   .1499172     -.0586691    .5295605
 (4 vs 1) 7  |   .1712098   .1912234     -.2039416    .5463611
 (4 vs 1) 8  |   .1069738    .237291     -.3585552    .5725029
--------------------------------------------------------------

.         estimates store american3

. reg american i.panel##c.immigration if outcome==2, robust                

Linear regression                               Number of obs     =      1,249
                                                F(7, 1241)        =      46.11
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2250
                                                Root MSE          =     1.1891

-------------------------------------------------------------------------------------
                    |               Robust
           american |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
              panel |
                 2  |  -.0023961   .0945653    -0.03   0.980    -.1879217    .1831295
                 3  |   -.011861   .0941901    -0.13   0.900    -.1966504    .1729285
                 4  |   .1322201   .0939722     1.41   0.160    -.0521419    .3165821
                    |
        immigration |  -.6940224   .0651156   -10.66   0.000    -.8217712   -.5662737
                    |
panel#c.immigration |
                 2  |   .0698675   .0946273     0.74   0.460    -.1157797    .2555148
                 3  |   .1375385   .1016674     1.35   0.176    -.0619206    .3369975
                 4  |   .0527048   .1021072     0.52   0.606    -.1476171    .2530266
                    |
              _cons |   3.636982   .0660059    55.10   0.000     3.507486    3.766477
-------------------------------------------------------------------------------------

.         margins r.panel if panel==1 | panel==4, at(immigration=(-1.5(.5)2)) post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()

1._at        : immigration     =        -1.5

2._at        : immigration     =          -1

3._at        : immigration     =         -.5

4._at        : immigration     =           0

5._at        : immigration     =          .5

6._at        : immigration     =           1

7._at        : immigration     =         1.5

8._at        : immigration     =           2

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
   panel@_at |
 (4 vs 1) 1  |          1        0.11     0.7349
 (4 vs 1) 2  |          1        0.45     0.5041
 (4 vs 1) 3  |          1        1.26     0.2617
 (4 vs 1) 4  |          1        1.98     0.1597
 (4 vs 1) 5  |          1        1.80     0.1802
 (4 vs 1) 6  |          1        1.40     0.2363
 (4 vs 1) 7  |          1        1.12     0.2906
 (4 vs 1) 8  |          1        0.93     0.3351
      Joint  |          2        1.00     0.3679
             |
 Denominator |       1241
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
   panel@_at |
 (4 vs 1) 1  |    .053163   .1569739     -.2548006    .3611265
 (4 vs 1) 2  |   .0795153   .1189868     -.1539222    .3129529
 (4 vs 1) 3  |   .1058677   .0942754      -.079089    .2908245
 (4 vs 1) 4  |   .1322201   .0939722     -.0521419    .3165821
 (4 vs 1) 5  |   .1585725   .1182652     -.0734493    .3905943
 (4 vs 1) 6  |   .1849249   .1560624     -.1212504    .4911001
 (4 vs 1) 7  |   .2112772    .199843     -.1807902    .6033447
 (4 vs 1) 8  |   .2376296   .2464385     -.2458525    .7211117
--------------------------------------------------------------

.         estimates store american4       

.                                 
. coefplot refugee1, bylabel("Non-critical Report of U.S.") || ///
>                  refugee2, bylabel("Critical Report of U.S.") ///
>                  yline(0) scheme(plottig) vertical ciopts(recast(rcap)) ///
>                  xtitle("White Nationalism") title("Outcome Good for Refugees") ///
>                  xlabel(1 "-1.5" 2 "-1" 3 "-0.5" 4 "0" 5 "0.5" 6 "1" 7 "1.5" 8 "2") ///
>                  ytitle("Marginal Effect")              
(note:  clockdir by_legend_position not found in scheme, default attributes used)

.                  graph save "wnat3.gph", replace
(note: file wnat3.gph not found)
(file wnat3.gph saved)

. 
. coefplot american1, bylabel("Non-critical Report of U.S.") || ///
>                  american2, bylabel("Critical Report of U.S.") ///
>                  yline(0) scheme(plottig) vertical ciopts(recast(rcap)) ///
>                  xtitle("White Nationalism") title("Outcome Good for Americans") ///
>                  xlabel(1 "-1.5" 2 "-1" 3 "-0.5" 4 "0" 5 "0.5" 6 "1" 7 "1.5" 8 "2") ///
>                  ytitle("Marginal Effect")              
(note:  clockdir by_legend_position not found in scheme, default attributes used)

.                  graph save "wnat4.gph", replace
(note: file wnat4.gph not found)
(file wnat4.gph saved)

. 
. graph combine "wnat3.gph" "wnat4.gph", ///
>           xcommon ycommon title("White Nationalism", size(med)) name(white2)
(note:  clockdir by_legend_position not found in scheme, default attributes used)
(note:  clockdir by_legend_position not found in scheme, default attributes used)
(note:  clockdir by_legend_position not found in scheme, default attributes used)
(note:  named style med not found in class gsize, default attributes used)

.           
. coefplot refugee3, bylabel("Non-critical Report of U.S.") || ///
>                  refugee4, bylabel("Critical Report of U.S.") ///
>                  yline(0) scheme(plottig) vertical ciopts(recast(rcap)) ///
>                  xtitle("Anti-Immigration Attitudes") title("Outcome Good for Refugees") ///
>                  xlabel(1 "-1.5" 2 "-1" 3 "-0.5" 4 "0" 5 "0.5" 6 "1" 7 "1.5" 8 "2") ///
>                  ytitle("Marginal Effect")              
(note:  clockdir by_legend_position not found in scheme, default attributes used)

.                  graph save "imm3.gph", replace
(note: file imm3.gph not found)
(file imm3.gph saved)

. 
. coefplot american3, bylabel("Non-critical Report of U.S.") || ///
>                  american4, bylabel("Critical Report of U.S.") ///
>                  yline(0) scheme(plottig) vertical ciopts(recast(rcap)) ///
>                  xtitle("Anti-Immigration Attitudes") title("Outcome Good for Americans") ///
>                  xlabel(1 "-1.5" 2 "-1" 3 "-0.5" 4 "0" 5 "0.5" 6 "1" 7 "1.5" 8 "2") ///
>                  ytitle("Marginal Effect")              
(note:  clockdir by_legend_position not found in scheme, default attributes used)

.                  graph save "imm4.gph", replace
(note: file imm4.gph not found)
(file imm4.gph saved)

. 
. graph combine "imm3.gph" "imm4.gph", ///
>           xcommon ycommon title("Anti-Immigration Attitudes", size(med)) name(imm2)              
(note:  clockdir by_legend_position not found in scheme, default attributes used)
(note:  clockdir by_legend_position not found in scheme, default attributes used)
(note:  clockdir by_legend_position not found in scheme, default attributes used)
(note:  named style med not found in class gsize, default attributes used)

. 
. graph combine imm2 white2, rows(2) scale(1.4)     
(note:  clockdir by_legend_position not found in scheme, default attributes used)
(note:  clockdir by_legend_position not found in scheme, default attributes used)
(note:  clockdir by_legend_position not found in scheme, default attributes used)
(note:  clockdir by_legend_position not found in scheme, default attributes used)
(note:  clockdir by_legend_position not found in scheme, default attributes used)
(note:  named style med not found in class gsize, default attributes used)
(note:  clockdir by_legend_position not found in scheme, default attributes used)
(note:  clockdir by_legend_position not found in scheme, default attributes used)
(note:  clockdir by_legend_position not found in scheme, default attributes used)
(note:  clockdir by_legend_position not found in scheme, default attributes used)
(note:  named style med not found in class gsize, default attributes used)

.                                                                 
.                                                                 
. *** Figure A11: Conditional Marginal Treatment Effects of Diversity, by Partisanship (2021)
. use "CH_followup.dta", clear

. 
. // american
. reg american i.panel##i.party, robust

Linear regression                               Number of obs     =      2,906
                                                F(11, 2894)       =       4.42
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0154
                                                Root MSE          =     1.2382

------------------------------------------------------------------------------
             |               Robust
    american |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |  -.0193099   .1079314    -0.18   0.858    -.2309401    .1923202
          3  |    .008606   .1043638     0.08   0.934     -.196029    .2132409
          4  |   .0642326   .1054469     0.61   0.542    -.1425261    .2709912
             |
       party |
          2  |   .2502655   .1090228     2.30   0.022     .0364953    .4640357
          3  |  -.0276476   .1115037    -0.25   0.804    -.2462823    .1909871
             |
 panel#party |
        2 2  |  -.0263472    .156127    -0.17   0.866    -.3324784    .2797841
        2 3  |    .112181   .1621454     0.69   0.489    -.2057511    .4301132
        3 2  |   .1259508   .1518525     0.83   0.407    -.1717992    .4237008
        3 3  |   .0191669   .1604577     0.12   0.905     -.295456    .3337897
        4 2  |    .101701   .1501258     0.68   0.498    -.1926634    .3960653
        4 3  |   .0111632   .1628485     0.07   0.945    -.3081476     .330474
             |
       _cons |   3.386831   .0730309    46.38   0.000     3.243633    3.530029
------------------------------------------------------------------------------

.         margins r.panel, at(party=1) vsquish post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()
at           : party           =           1

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        0.03     0.8580
   (3 vs 1)  |          1        0.01     0.9343
   (4 vs 1)  |          1        0.37     0.5425
      Joint  |          3        0.22     0.8840
             |
 Denominator |       2894
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |  -.0193099   .1079314     -.2309401    .1923202
   (3 vs 1)  |    .008606   .1043638      -.196029    .2132409
   (4 vs 1)  |   .0642326   .1054469     -.1425261    .2709912
--------------------------------------------------------------

.         estimates store ind1

. reg american i.panel##i.party, robust

Linear regression                               Number of obs     =      2,906
                                                F(11, 2894)       =       4.42
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0154
                                                Root MSE          =     1.2382

------------------------------------------------------------------------------
             |               Robust
    american |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |  -.0193099   .1079314    -0.18   0.858    -.2309401    .1923202
          3  |    .008606   .1043638     0.08   0.934     -.196029    .2132409
          4  |   .0642326   .1054469     0.61   0.542    -.1425261    .2709912
             |
       party |
          2  |   .2502655   .1090228     2.30   0.022     .0364953    .4640357
          3  |  -.0276476   .1115037    -0.25   0.804    -.2462823    .1909871
             |
 panel#party |
        2 2  |  -.0263472    .156127    -0.17   0.866    -.3324784    .2797841
        2 3  |    .112181   .1621454     0.69   0.489    -.2057511    .4301132
        3 2  |   .1259508   .1518525     0.83   0.407    -.1717992    .4237008
        3 3  |   .0191669   .1604577     0.12   0.905     -.295456    .3337897
        4 2  |    .101701   .1501258     0.68   0.498    -.1926634    .3960653
        4 3  |   .0111632   .1628485     0.07   0.945    -.3081476     .330474
             |
       _cons |   3.386831   .0730309    46.38   0.000     3.243633    3.530029
------------------------------------------------------------------------------

.         margins r.panel, at(party=2) vsquish post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()
at           : party           =           2

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        0.16     0.6857
   (3 vs 1)  |          1        1.49     0.2226
   (4 vs 1)  |          1        2.41     0.1206
      Joint  |          3        1.85     0.1363
             |
 Denominator |       2894
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |  -.0456571   .1128115     -.2668562     .175542
   (3 vs 1)  |   .1345568   .1103058     -.0817292    .3508427
   (4 vs 1)  |   .1659335   .1068584     -.0435927    .3754597
--------------------------------------------------------------

.         estimates store dem1

. reg american i.panel##i.party, robust

Linear regression                               Number of obs     =      2,906
                                                F(11, 2894)       =       4.42
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0154
                                                Root MSE          =     1.2382

------------------------------------------------------------------------------
             |               Robust
    american |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |  -.0193099   .1079314    -0.18   0.858    -.2309401    .1923202
          3  |    .008606   .1043638     0.08   0.934     -.196029    .2132409
          4  |   .0642326   .1054469     0.61   0.542    -.1425261    .2709912
             |
       party |
          2  |   .2502655   .1090228     2.30   0.022     .0364953    .4640357
          3  |  -.0276476   .1115037    -0.25   0.804    -.2462823    .1909871
             |
 panel#party |
        2 2  |  -.0263472    .156127    -0.17   0.866    -.3324784    .2797841
        2 3  |    .112181   .1621454     0.69   0.489    -.2057511    .4301132
        3 2  |   .1259508   .1518525     0.83   0.407    -.1717992    .4237008
        3 3  |   .0191669   .1604577     0.12   0.905     -.295456    .3337897
        4 2  |    .101701   .1501258     0.68   0.498    -.1926634    .3960653
        4 3  |   .0111632   .1628485     0.07   0.945    -.3081476     .330474
             |
       _cons |   3.386831   .0730309    46.38   0.000     3.243633    3.530029
------------------------------------------------------------------------------

.         margins r.panel, at(party=3) vsquish post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()
at           : party           =           3

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        0.59     0.4428
   (3 vs 1)  |          1        0.05     0.8198
   (4 vs 1)  |          1        0.37     0.5435
      Joint  |          3        0.24     0.8650
             |
 Denominator |       2894
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |   .0928711   .1210039     -.1443915    .3301337
   (3 vs 1)  |   .0277728   .1218805     -.2112085    .2667542
   (4 vs 1)  |   .0753958   .1240991     -.1679359    .3187274
--------------------------------------------------------------

.         estimates store rep1    

. // refugee      
. reg refugee i.panel##i.party, robust

Linear regression                               Number of obs     =      2,906
                                                F(11, 2894)       =       3.22
                                                Prob > F          =     0.0002
                                                R-squared         =     0.0120
                                                Root MSE          =     1.2577

------------------------------------------------------------------------------
             |               Robust
     refugee |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |   .1239316   .1151271     1.08   0.282    -.1018078     .349671
          3  |   .0971694   .1100118     0.88   0.377      -.11854    .3128788
          4  |   .3309693   .1099583     3.01   0.003     .1153649    .5465737
             |
       party |
          2  |   .2616487   .1198684     2.18   0.029     .0266127    .4966848
          3  |     .10839   .1145687     0.95   0.344    -.1162544    .3330345
             |
 panel#party |
        2 2  |  -.0747287   .1661995    -0.45   0.653      -.40061    .2511527
        2 3  |   .0477392   .1648259     0.29   0.772    -.2754488    .3709273
        3 2  |    .127621   .1621491     0.79   0.431    -.1903183    .4455604
        3 3  |   .0026531   .1585235     0.02   0.987    -.3081772    .3134835
        4 2  |   -.167113   .1605357    -1.04   0.298    -.4818888    .1476629
        4 3  |  -.1522357   .1606215    -0.95   0.343    -.4671798    .1627083
             |
       _cons |   3.222222   .0812284    39.67   0.000     3.062951    3.381494
------------------------------------------------------------------------------

.         margins r.panel, at(party=1) vsquish post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()
at           : party           =           1

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        1.16     0.2818
   (3 vs 1)  |          1        0.78     0.3772
   (4 vs 1)  |          1        9.06     0.0026
      Joint  |          3        3.32     0.0190
             |
 Denominator |       2894
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |   .1239316   .1151271     -.1018078     .349671
   (3 vs 1)  |   .0971694   .1100118       -.11854    .3128788
   (4 vs 1)  |   .3309693   .1099583      .1153649    .5465737
--------------------------------------------------------------

.         estimates store ind2

. reg refugee i.panel##i.party##outcome, robust

Linear regression                               Number of obs     =      2,906
                                                F(23, 2882)       =      12.63
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0837
                                                Root MSE          =     1.2137

-------------------------------------------------------------------------------------
                    |               Robust
            refugee |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
              panel |
                 2  |   .1363957   .1580383     0.86   0.388    -.1734839    .4462752
                 3  |   .1334441   .1549916     0.86   0.389    -.1704616    .4373497
                 4  |   .4554581   .1536532     2.96   0.003     .1541769    .7567394
                    |
              party |
                 2  |   .0620155   .1612699     0.38   0.701    -.2542006    .3782316
                 3  |   .4671879    .163643     2.85   0.004     .1463187    .7880571
                    |
        panel#party |
               2 2  |  -.0557505   .2280529    -0.24   0.807    -.5029138    .3914128
               2 3  |   .1780581   .2293195     0.78   0.438    -.2715888    .6277049
               3 2  |   .0520398   .2261757     0.23   0.818    -.3914427    .4955223
               3 3  |   .0873998   .2224643     0.39   0.694    -.3488054     .523605
               4 2  |  -.2021248   .2171573    -0.93   0.352    -.6279241    .2236745
               4 3  |  -.2936202   .2299817    -1.28   0.202    -.7445656    .1573251
                    |
          2.outcome |   .6058752   .1575433     3.85   0.000     .2969662    .9147841
                    |
      panel#outcome |
               2 2  |  -.0430872   .2240978    -0.19   0.848    -.4824953     .396321
               3 2  |  -.1298585   .2147957    -0.60   0.546    -.5510271    .2913102
               4 2  |  -.2736541   .2153031    -1.27   0.204    -.6958177    .1485096
                    |
      party#outcome |
               2 2  |    .536982   .2230147     2.41   0.016     .0996975    .9742665
               3 2  |  -.7474817   .2260221    -3.31   0.001    -1.190663   -.3043003
                    |
panel#party#outcome |
             2 2 2  |  -.2255279   .3144378    -0.72   0.473    -.8420735    .3910177
             2 3 2  |  -.2402896   .3243877    -0.74   0.459    -.8763449    .3957657
             3 2 2  |   .0092098   .3048778     0.03   0.976    -.5885908    .6070103
             3 3 2  |  -.1489438   .3130625    -0.48   0.634    -.7627928    .4649052
             4 2 2  |   .0441302   .3023637     0.15   0.884    -.5487407    .6370012
             4 3 2  |   .3097887   .3185153     0.97   0.331     -.314752    .9343295
                    |
              _cons |   2.937984   .1126697    26.08   0.000     2.717063    3.158906
-------------------------------------------------------------------------------------

.         margins r.panel, at(party=2) vsquish post

Contrasts of predictive margins
Model VCE    : Robust

Expression   : Linear prediction, predict()
at           : party           =           2

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        0.21     0.6447
   (3 vs 1)  |          1        1.36     0.2445
   (4 vs 1)  |          1        1.76     0.1851
      Joint  |          3        1.58     0.1929
             |
 Denominator |       2882
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |  -.0509818   .1105517     -.2677502    .1657866
   (3 vs 1)  |   .1263635   .1085466     -.0864732    .3392002
   (4 vs 1)  |   .1408619    .106263     -.0674972     .349221
--------------------------------------------------------------

.         estimates store dem2

. reg refugee i.panel##i.party##outcome, robust

Linear regression                               Number of obs     =      2,906
                                                F(23, 2882)       =      12.63
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0837
                                                Root MSE          =     1.2137

-------------------------------------------------------------------------------------
                    |               Robust
            refugee |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
              panel |
                 2  |   .1363957   .1580383     0.86   0.388    -.1734839    .4462752
                 3  |   .1334441   .1549916     0.86   0.389    -.1704616    .4373497
                 4  |   .4554581   .1536532     2.96   0.003     .1541769    .7567394
                    |
              party |
                 2  |   .0620155   .1612699     0.38   0.701    -.2542006    .3782316
                 3  |   .4671879    .163643     2.85   0.004     .1463187    .7880571
                    |
        panel#party |
               2 2  |  -.0557505   .2280529    -0.24   0.807    -.5029138    .3914128
               2 3  |   .1780581   .2293195     0.78   0.438    -.2715888    .6277049
               3 2  |   .0520398   .2261757     0.23   0.818    -.3914427    .4955223
               3 3  |   .0873998   .2224643     0.39   0.694    -.3488054     .523605
               4 2  |  -.2021248   .2171573    -0.93   0.352    -.6279241    .2236745
               4 3  |  -.2936202   .2299817    -1.28   0.202    -.7445656    .1573251
                    |
          2.outcome |   .6058752   .1575433     3.85   0.000     .2969662    .9147841
                    |
      panel#outcome |
               2 2  |  -.0430872   .2240978    -0.19   0.848    -.4824953     .396321
               3 2  |  -.1298585   .2147957    -0.60   0.546    -.5510271    .2913102
               4 2  |  -.2736541   .2153031    -1.27   0.204    -.6958177    .1485096
                    |
      party#outcome |
               2 2  |    .536982   .2230147     2.41   0.016     .0996975    .9742665
               3 2  |  -.7474817   .2260221    -3.31   0.001    -1.190663   -.3043003
                    |
panel#party#outcome |
             2 2 2  |  -.2255279   .3144378    -0.72   0.473    -.8420735    .3910177
             2 3 2  |  -.2402896   .3243877    -0.74   0.459    -.8763449    .3957657
             3 2 2  |   .0092098   .3048778     0.03   0.976    -.5885908    .6070103
             3 3 2  |  -.1489438   .3130625    -0.48   0.634    -.7627928    .4649052
             4 2 2  |   .0441302   .3023637     0.15   0.884    -.5487407    .6370012
             4 3 2  |   .3097887   .3185153     0.97   0.331     -.314752    .9343295
                    |
              _cons |   2.937984   .1126697    26.08   0.000     2.717063    3.158906
-------------------------------------------------------------------------------------

.         margins r.panel, at(party=3) vsquish post

Contrasts of predictive margins
Model VCE    : Robust

Expression   : Linear prediction, predict()
at           : party           =           3

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        2.24     0.1345
   (3 vs 1)  |          1        0.55     0.4595
   (4 vs 1)  |          1        2.33     0.1267
      Joint  |          3        1.07     0.3615
             |
 Denominator |       2882
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |   .1755933    .117301      -.054409    .4055955
   (3 vs 1)  |    .084225   .1138577     -.1390257    .3074757
   (4 vs 1)  |   .1795446    .117534     -.0509145    .4100038
--------------------------------------------------------------

.         estimates store rep2

. // fair
. reg fair i.panel##i.party, robust

Linear regression                               Number of obs     =      2,906
                                                F(11, 2894)       =       9.83
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0322
                                                Root MSE          =     1.1127

------------------------------------------------------------------------------
             |               Robust
        fair |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |   .2022792   .0984755     2.05   0.040       .00919    .3953684
          3  |   .1750458   .0995035     1.76   0.079    -.0200591    .3701506
          4  |    .400788   .0955423     4.19   0.000     .2134502    .5881258
             |
       party |
          2  |   .2268519   .1045008     2.17   0.030     .0219483    .4317554
          3  |   .1824641    .103447     1.76   0.078     -.020373    .3853012
             |
 panel#party |
        2 2  |  -.0675516   .1442811    -0.47   0.640    -.3504557    .2153525
        2 3  |  -.0077773   .1466234    -0.05   0.958     -.295274    .2797194
        3 2  |   .1113716   .1461877     0.76   0.446     -.175271    .3980141
        3 3  |  -.0143537   .1449222    -0.10   0.921    -.2985148    .2698074
        4 2  |   .1636059   .1369358     1.19   0.232    -.1048957    .4321075
        4 3  |  -.0538302   .1416964    -0.38   0.704    -.3316663    .2240059
             |
       _cons |   3.148148   .0703056    44.78   0.000     3.010294    3.286002
------------------------------------------------------------------------------

.         margins r.panel, at(party=1) vsquish post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()
at           : party           =           1

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        4.22     0.0401
   (3 vs 1)  |          1        3.09     0.0787
   (4 vs 1)  |          1       17.60     0.0000
      Joint  |          3        5.95     0.0005
             |
 Denominator |       2894
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |   .2022792   .0984755        .00919    .3953684
   (3 vs 1)  |   .1750458   .0995035     -.0200591    .3701506
   (4 vs 1)  |    .400788   .0955423      .2134502    .5881258
--------------------------------------------------------------

.         estimates store ind3

. reg fair i.panel##i.party, robust

Linear regression                               Number of obs     =      2,906
                                                F(11, 2894)       =       9.83
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0322
                                                Root MSE          =     1.1127

------------------------------------------------------------------------------
             |               Robust
        fair |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |   .2022792   .0984755     2.05   0.040       .00919    .3953684
          3  |   .1750458   .0995035     1.76   0.079    -.0200591    .3701506
          4  |    .400788   .0955423     4.19   0.000     .2134502    .5881258
             |
       party |
          2  |   .2268519   .1045008     2.17   0.030     .0219483    .4317554
          3  |   .1824641    .103447     1.76   0.078     -.020373    .3853012
             |
 panel#party |
        2 2  |  -.0675516   .1442811    -0.47   0.640    -.3504557    .2153525
        2 3  |  -.0077773   .1466234    -0.05   0.958     -.295274    .2797194
        3 2  |   .1113716   .1461877     0.76   0.446     -.175271    .3980141
        3 3  |  -.0143537   .1449222    -0.10   0.921    -.2985148    .2698074
        4 2  |   .1636059   .1369358     1.19   0.232    -.1048957    .4321075
        4 3  |  -.0538302   .1416964    -0.38   0.704    -.3316663    .2240059
             |
       _cons |   3.148148   .0703056    44.78   0.000     3.010294    3.286002
------------------------------------------------------------------------------

.         margins r.panel, at(party=2) vsquish post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()
at           : party           =           2

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        1.63     0.2015
   (3 vs 1)  |          1        7.15     0.0075
   (4 vs 1)  |          1       33.10     0.0000
      Joint  |          3       13.13     0.0000
             |
 Denominator |       2894
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |   .1347276   .1054496     -.0720362    .3414915
   (3 vs 1)  |   .2864173   .1070977      .0764219    .4964127
   (4 vs 1)  |   .5643939   .0980973      .3720462    .7567416
--------------------------------------------------------------

.         estimates store dem3

. reg fair i.panel##i.party, robust

Linear regression                               Number of obs     =      2,906
                                                F(11, 2894)       =       9.83
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0322
                                                Root MSE          =     1.1127

------------------------------------------------------------------------------
             |               Robust
        fair |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |   .2022792   .0984755     2.05   0.040       .00919    .3953684
          3  |   .1750458   .0995035     1.76   0.079    -.0200591    .3701506
          4  |    .400788   .0955423     4.19   0.000     .2134502    .5881258
             |
       party |
          2  |   .2268519   .1045008     2.17   0.030     .0219483    .4317554
          3  |   .1824641    .103447     1.76   0.078     -.020373    .3853012
             |
 panel#party |
        2 2  |  -.0675516   .1442811    -0.47   0.640    -.3504557    .2153525
        2 3  |  -.0077773   .1466234    -0.05   0.958     -.295274    .2797194
        3 2  |   .1113716   .1461877     0.76   0.446     -.175271    .3980141
        3 3  |  -.0143537   .1449222    -0.10   0.921    -.2985148    .2698074
        4 2  |   .1636059   .1369358     1.19   0.232    -.1048957    .4321075
        4 3  |  -.0538302   .1416964    -0.38   0.704    -.3316663    .2240059
             |
       _cons |   3.148148   .0703056    44.78   0.000     3.010294    3.286002
------------------------------------------------------------------------------

.         margins r.panel, at(party=3) vsquish post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()
at           : party           =           3

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        3.21     0.0735
   (3 vs 1)  |          1        2.33     0.1273
   (4 vs 1)  |          1       10.99     0.0009
      Joint  |          3        3.70     0.0113
             |
 Denominator |       2894
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |   .1945019   .1086323     -.0185026    .4075065
   (3 vs 1)  |   .1606921   .1053636     -.0459032    .3672874
   (4 vs 1)  |   .3469578   .1046401      .1417812    .5521345
--------------------------------------------------------------

.         estimates store rep3            

. // trust        
. reg trust i.panel##i.party, robust

Linear regression                               Number of obs     =      2,906
                                                F(11, 2894)       =       6.47
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0228
                                                Root MSE          =      1.191

------------------------------------------------------------------------------
             |               Robust
       trust |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |   .0642608   .1059448     0.61   0.544     -.143474    .2719957
          3  |   .0748877   .1030787     0.73   0.468    -.1272273    .2770027
          4  |   .2847737   .1030942     2.76   0.006     .0826283     .486919
             |
       party |
          2  |   .1952575   .1080528     1.81   0.071    -.0166108    .4071258
          3  |   .0766104   .1085243     0.71   0.480    -.1361823    .2894031
             |
 panel#party |
        2 2  |   .0882903    .152793     0.58   0.563    -.2113038    .3878844
        2 3  |   .1776924   .1575458     1.13   0.259    -.1312209    .4866057
        3 2  |   .1185654   .1509516     0.79   0.432     -.177418    .4145488
        3 3  |   .0767538   .1519589     0.51   0.614    -.2212049    .3747125
        4 2  |    .169894   .1474607     1.15   0.249    -.1192445    .4590325
        4 3  |   .0467541   .1528859     0.31   0.760    -.2530221    .3465303
             |
       _cons |   3.115226   .0736264    42.31   0.000     2.970861    3.259592
------------------------------------------------------------------------------

.         margins r.panel, at(party=1) vsquish post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()
at           : party           =           1

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        0.37     0.5442
   (3 vs 1)  |          1        0.53     0.4676
   (4 vs 1)  |          1        7.63     0.0058
      Joint  |          3        2.90     0.0338
             |
 Denominator |       2894
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |   .0642608   .1059448      -.143474    .2719957
   (3 vs 1)  |   .0748877   .1030787     -.1272273    .2770027
   (4 vs 1)  |   .2847737   .1030942      .0826283     .486919
--------------------------------------------------------------

.         estimates store ind4

. reg trust i.panel##i.party, robust

Linear regression                               Number of obs     =      2,906
                                                F(11, 2894)       =       6.47
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0228
                                                Root MSE          =      1.191

------------------------------------------------------------------------------
             |               Robust
       trust |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |   .0642608   .1059448     0.61   0.544     -.143474    .2719957
          3  |   .0748877   .1030787     0.73   0.468    -.1272273    .2770027
          4  |   .2847737   .1030942     2.76   0.006     .0826283     .486919
             |
       party |
          2  |   .1952575   .1080528     1.81   0.071    -.0166108    .4071258
          3  |   .0766104   .1085243     0.71   0.480    -.1361823    .2894031
             |
 panel#party |
        2 2  |   .0882903    .152793     0.58   0.563    -.2113038    .3878844
        2 3  |   .1776924   .1575458     1.13   0.259    -.1312209    .4866057
        3 2  |   .1185654   .1509516     0.79   0.432     -.177418    .4145488
        3 3  |   .0767538   .1519589     0.51   0.614    -.2212049    .3747125
        4 2  |    .169894   .1474607     1.15   0.249    -.1192445    .4590325
        4 3  |   .0467541   .1528859     0.31   0.760    -.2530221    .3465303
             |
       _cons |   3.115226   .0736264    42.31   0.000     2.970861    3.259592
------------------------------------------------------------------------------

.         margins r.panel, at(party=2) vsquish post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()
at           : party           =           2

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        1.92     0.1660
   (3 vs 1)  |          1        3.08     0.0795
   (4 vs 1)  |          1       18.60     0.0000
      Joint  |          3        6.65     0.0002
             |
 Denominator |       2894
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |   .1525511   .1100973     -.0633259    .3684282
   (3 vs 1)  |   .1934531   .1102777     -.0227776    .4096838
   (4 vs 1)  |   .4546676   .1054336      .2479352    .6614001
--------------------------------------------------------------

.         estimates store dem4

. reg trust i.panel##i.party, robust

Linear regression                               Number of obs     =      2,906
                                                F(11, 2894)       =       6.47
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0228
                                                Root MSE          =      1.191

------------------------------------------------------------------------------
             |               Robust
       trust |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       panel |
          2  |   .0642608   .1059448     0.61   0.544     -.143474    .2719957
          3  |   .0748877   .1030787     0.73   0.468    -.1272273    .2770027
          4  |   .2847737   .1030942     2.76   0.006     .0826283     .486919
             |
       party |
          2  |   .1952575   .1080528     1.81   0.071    -.0166108    .4071258
          3  |   .0766104   .1085243     0.71   0.480    -.1361823    .2894031
             |
 panel#party |
        2 2  |   .0882903    .152793     0.58   0.563    -.2113038    .3878844
        2 3  |   .1776924   .1575458     1.13   0.259    -.1312209    .4866057
        3 2  |   .1185654   .1509516     0.79   0.432     -.177418    .4145488
        3 3  |   .0767538   .1519589     0.51   0.614    -.2212049    .3747125
        4 2  |    .169894   .1474607     1.15   0.249    -.1192445    .4590325
        4 3  |   .0467541   .1528859     0.31   0.760    -.2530221    .3465303
             |
       _cons |   3.115226   .0736264    42.31   0.000     2.970861    3.259592
------------------------------------------------------------------------------

.         margins r.panel, at(party=3) vsquish post

Contrasts of adjusted predictions
Model VCE    : Robust

Expression   : Linear prediction, predict()
at           : party           =           3

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
       panel |
   (2 vs 1)  |          1        4.31     0.0381
   (3 vs 1)  |          1        1.84     0.1745
   (4 vs 1)  |          1        8.62     0.0033
      Joint  |          3        3.12     0.0251
             |
 Denominator |       2894
------------------------------------------------

--------------------------------------------------------------
             |            Delta-method
             |   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       panel |
   (2 vs 1)  |   .2419532   .1166035      .0133189    .4705876
   (3 vs 1)  |   .1516415   .1116526     -.0672852    .3705682
   (4 vs 1)  |   .3315278   .1128968      .1101615     .552894
--------------------------------------------------------------

.         estimates store rep4    

.                         
. coefplot (dem3, color(538b) ciopts(lcolor(538b) recast(rcap))) ///
>          (rep3, color(538r) ciopts(lcolor(538r) recast(rcap))) ///
>                  (ind3, color(538g) ciopts(lcolor(538g) recast(rcap))), ///
>                   bylabel("UNHCR Panel Is Fair") || ///
>                  (dem4, color(538b) ciopts(lcolor(538b) recast(rcap))) ///
>          (rep4, color(538r) ciopts(lcolor(538r) recast(rcap))) ///
>                  (ind4, color(538g) ciopts(lcolor(538g) recast(rcap))), ///
>                   bylabel("UNHCR Panel Can Be Trusted") || ///
>                  (dem1, color(538b) ciopts(lcolor(538b) recast(rcap))) ///
>          (rep1, color(538r) ciopts(lcolor(538r) recast(rcap))) ///
>                  (ind1, color(538g) ciopts(lcolor(538g) recast(rcap))), ///
>                   bylabel("Outcome Is Good for American") || ///
>                  (dem2, color(538b) ciopts(lcolor(538b) recast(rcap))) ///
>          (rep2, color(538r) ciopts(lcolor(538r) recast(rcap))) ///
>                  (ind2, color(538g) ciopts(lcolor(538g) recast(rcap))), ///
>                   bylabel("Outcome Is Good for Refugee") vertical ///
>                  yline(0) ytitle("Marginal Difference (All-White, All-Male vs. Diverse Panels") ///
>                  xtitle("Racial and Gender Distribution of Panel (All-White, All-Male as Reference 
> Group)") ///
>                  xlabel(1 `""Mixed-Race" "All-Male""' 2 `""All-White" "Mixed-Gender""' ///
>                             3 `""Mixed-Race" "Mixed-Gender""', ///
>                  labcol(black)) scheme(plottig) ///
>                  legend(order(2 "Democrats" 4 "Republicans" 6 "Independents") rows(1)) 
(note:  clockdir by_legend_position not found in scheme, default attributes used)

.           
. 
end of do-file

. exit, clear
